# Summary tables
# Variables labels are NOT assigned -- need to modify dictionaries above 
# when adding or removing variables from summary tables
allcompdf <- as.data.frame(allcomp)
allcompccdf <- as.data.frame(allcomp_cc)

stargazer(allcompdf[c("active_mines" ,"uer","employed", "unemployed", 
                      "labour_force", "pop", "REE_inv","production_shorttons",
                      "realgdp","realgdp_pc", "ruc", "ruc_bin","total_taa",
                      "REE_inv_scaled_realgdp")], title = "Summary Statistics: Contiguous US Counties", 
                      covariate.labels = sum_dict)

% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Wed, May 25, 2022 - 17:54:01
\begin{table}[!htbp] \centering 
  \caption{Summary Statistics: Contiguous US Counties} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lccccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
Statistic & \multicolumn{1}{c}{N} & \multicolumn{1}{c}{Mean} & \multicolumn{1}{c}{St. Dev.} & \multicolumn{1}{c}{Min} & \multicolumn{1}{c}{Pctl(25)} & \multicolumn{1}{c}{Pctl(75)} & \multicolumn{1}{c}{Max} \\ 
\hline \\[-1.8ex] 
Active Mines (no.) & 55,296 & 0.462 & 3.723 & 0 & 0 & 0 & 114 \\ 
Unemployment Rate & 55,296 & 6.130 & 2.720 & 0.800 & 4.200 & 7.500 & 29.400 \\ 
Employed Persons & 55,296 & 46,504.600 & 149,661.300 & 34 & 4,799 & 30,140.2 & 4,888,581 \\ 
Unemployed Persons & 55,296 & 2,993.569 & 11,183.150 & 3 & 293 & 1,988.2 & 621,950 \\ 
Labour Force & 55,296 & 49,498.160 & 159,938.100 & 38 & 5,128 & 32,055.2 & 5,122,843 \\ 
Population & 55,296 & 99,440.810 & 318,828.000 & 55 & 11,223.5 & 67,553.2 & 10,105,708 \\ 
Total USDA RE Investments in USD & 55,296 & 114,142.500 & 2,670,894.000 & 0 & 0 & 0 & 250,000,000 \\ 
Coal Production (in short tons) & 55,296 & 331,540.000 & 6,456,768.000 & 0 & 0 & 0 & 415,924,096 \\ 
Real GDP & 55,296 & 5,140,646.000 & 22,046,739.000 & 7,648 & 342,473 & 2,550,861 & 726,943,301 \\ 
Real GDP Per Capita & 55,296 & 50.845 & 463.903 & 5.787 & 25.894 & 46.756 & 59,848.920 \\ 
Rural-Urban Code & 55,296 & 5.090 & 2.685 & 1 & 3 & 7 & 9 \\ 
Rural-Urban (binary) & 55,296 & 0.648 & 0.477 & 0 & 0 & 1 & 1 \\ 
Total TAA Allocation in USD & 55,296 & 11,847,722.000 & 17,737,632.000 & 0 & 0 & 18,184,526 & 128,190,686 \\ 
USDA RE Investments scaled by Real GDP & 55,296 & 0.091 & 3.073 & 0 & 0 & 0 & 504 \\ 
\hline \\[-1.8ex] 
\end{tabular} 
\end{table} 
stargazer(allcompccdf[c("active_mines" ,"uer","employed", "unemployed", 
                        "labour_force", "pop", "REE_inv","production_shorttons",
                        "realgdp","realgdp_pc", "ruc", "ruc_bin","total_taa",
                        "REE_inv_scaled_realgdp")], title = "Summary Statistics: US Coal Counties",
                        covariate.labels = sum_dict, type = "text")

Summary Statistics: US Coal Counties
===================================================================================================================
Statistic                                N        Mean         St. Dev.     Min   Pctl(25)   Pctl(75)       Max    
-------------------------------------------------------------------------------------------------------------------
Active Mines (no.)                     4,518     5.653          11.847       0        1          5          114    
Unemployment Rate                      4,518     6.946          2.520        2       5.2        8.3         21     
Employed Persons                       4,518   29,069.860     58,838.490    816    6,859.2   27,426.2     622,714  
Unemployed Persons                     4,518   1,933.251      3,535.003      32      477      1,889.8     46,564   
Labour Force                           4,518   31,003.110     62,186.670    870    7,391.8   29,620.5     651,926  
Population                             4,518   64,866.890    119,765.600   1,836  16,720.8   64,536.8    1,265,577 
Total USDA RE Investments in USD       4,518   27,055.950    333,439.800     0        0          0      10,005,017 
Coal Production (in short tons)        4,518 4,057,556.000  22,253,668.000   0        0     3,564,906.0 415,924,096
Real GDP                               4,518 3,045,039.000  7,913,160.000  47,597 590,750.2  2,623,148  92,984,370 
Real GDP Per Capita                    4,518     41.688         25.763     8.221   26.348     48.551      244.161  
Rural-Urban Code                       4,518     5.116          2.376        1        3          7           9     
Rural-Urban (binary)                   4,518     0.697          0.460        0        0          1           1     
Total TAA Allocation in USD            4,518 14,597,789.000 20,533,595.000   0        0     23,427,230  117,476,517
USDA RE Investments scaled by Real GDP 4,518     0.016          0.241        0        0          0           9     
-------------------------------------------------------------------------------------------------------------------
stargazer(allcompdf[c("diff_uer", "mines_diff","diff_log_realgdp", 
                      "diff_log_realgdp_pc", "diff_log_employed",
                      "diff_log_unemployed", "diff_log_lf", "diff_log_pop", 
                      "REE_bin_top90") ], 
                      title = "Summary Statistics of Transformed Variables: Contiguous US Counties",
                      covariate.labels = sum_dict_trans, type = "text")

Summary Statistics of Transformed Variables: Contiguous US Counties
==================================================================================
Statistic                     N     Mean  St. Dev.  Min   Pctl(25) Pctl(75)  Max  
----------------------------------------------------------------------------------
Δ Unemployment Rate         55,296 -0.061  1.228   -8.600  -0.700   0.300   13.500
Δ Active Mines              55,296 -0.015  0.605    -30      0        0       20  
Δ (log) Real GDP            55,295 0.017   0.088   -1.122  -0.017   0.048   1.386 
Δ (log) Real GDP per capita 55,295 0.014   0.088   -1.126  -0.020   0.043   1.391 
Δ (log) Employed Persons    55,296 0.001   0.037   -0.596  -0.013   0.018   1.022 
Δ (log) Unemployed Persons  55,296 -0.013  0.177   -0.852  -0.124   0.057   1.305 
Δ (log) Labour Force        55,296 0.001   0.034   -0.562  -0.013   0.015   0.986 
Δ (log) Population          55,295 0.003   0.014   -0.425  -0.005   0.009   0.290 
REE ≥ .1% of GDP            55,296 0.127   0.333     0       0        0       1   
----------------------------------------------------------------------------------
stargazer(allcompccdf[c("diff_uer", "mines_diff","diff_log_realgdp", 
                        "diff_log_realgdp_pc", "diff_log_employed",
                        "diff_log_unemployed", "diff_log_lf", "diff_log_pop", 
                        "REE_bin_top90") ], 
                        title = "Summary Statistics of Transformed Variables: US Coal Counties Subset", 
                        covariate.labels = sum_dict_trans, type = "text")

Summary Statistics of Transformed Variables: US Coal Counties Subset
=================================================================================
Statistic                     N    Mean   St. Dev.  Min   Pctl(25) Pctl(75)  Max 
---------------------------------------------------------------------------------
Δ Unemployment Rate         4,518 -0.056   1.322   -4.600  -0.793   0.400   8.300
Δ Active Mines              4,518 -0.182   2.109    -30      0        0      20  
Δ (log) Real GDP            4,518  0.010   0.081   -0.604  -0.022   0.038   1.342
Δ (log) Real GDP per capita 4,518  0.010   0.080   -0.594  -0.021   0.037   1.351
Δ (log) Employed Persons    4,518 -0.002   0.035   -0.440  -0.015   0.014   0.214
Δ (log) Unemployed Persons  4,518 -0.014   0.179   -0.479  -0.120   0.051   1.077
Δ (log) Labour Force        4,518 -0.003   0.031   -0.396  -0.015   0.012   0.185
Δ (log) Population          4,518 -0.0004  0.010   -0.098  -0.006   0.004   0.066
REE ≥ .1% of GDP            4,518  0.097   0.296     0       0        0       1  
---------------------------------------------------------------------------------

1 FIXEST regression results

Overall Research Question: Do various indicators for changes in coal activity impact various county-level employment indicators?

Employs standard two-way fixed effects panel regressions with county-clustered standard errors to a panel dataset of 3,072 contiguous counties observed between 2002-2019.

1.1 Employment variables regressed on various measures of coal activity

1.1.1 Change in active mines

Results:

  1. Models 1: Significant change in unemployment rate in time t (in expected direction). Impact is near zero (albeit consistent sign as in t) in t+1 and changes sign in t+2 indicating a level effect as opposed to a growth effect. A one-unit decrease (increase) in active mines is associated with an increase (decrease) in the unemployment rate of 0.06 percentage points, negligible change in t+1 of the same direction, with a sign change and decrease(boost) of 0.03 percentage points. 1% significance level in time t. Indicates a non-growth effect with initial shock in time t (potentially t+1) but correcting again in t+2. (same conclusion for subset of coal counties).

  2. Models 2/3: Change in employed/unemployed persons is also significant and in the expected direction. A one-unit increase in active mines is associated with a 0.12% increase in the total number of employed persons in time t (5% significance level) and 0.17% in time t+1 at the 1% significance level. (same conclusion for subset of coal counties)

  3. Models 4: Change in labour force is only significant in t+1 indicating that individuals might leave the labour force following a mine closure. Though the result is very small… (same conclusion for subset of coal counties)

  4. Models 5: Change in population is negligible (both in significance and magnitude). (same conclusion for subset of coal counties)

# Level UER and level mines with various lags
lev22 <- feols(uer ~ l(active_mines, -2:0) + lag_mines + lag_mines2 + log_realgdp_pc | 
               fips + year, allcomp, panel.id = ~fips+year, se = 'twoway')
lev33 <- feols(uer ~ l(active_mines, -3:0) + lag_mines + lag_mines2 + lag_mines3 + log_realgdp_pc | 
               fips + year, allcomp, panel.id = ~fips+year, se = 'twoway')
lev03 <- feols(uer ~ active_mines + lag_mines +lag_mines2 + lag_mines3 + log_realgdp_pc | 
               fips + year, allcomp, se = 'twoway')
lev02 <- feols(uer ~ active_mines + lag_mines +lag_mines2 + log_realgdp_pc | 
               fips + year, allcomp, se = 'twoway')

# Level UER and mine levels on coal county subset
lev22cc <- feols(uer ~ l(active_mines, -2:0) + lag_mines + lag_mines2 + log_realgdp_pc | 
               fips + year, allcomp_cc, panel.id = ~fips+year, se = 'twoway')
lev33cc <- feols(uer ~ l(active_mines, -3:0) + lag_mines + lag_mines2 + lag_mines3 + log_realgdp_pc | 
               fips + year, allcomp_cc, panel.id = ~fips+year, se = 'twoway')
lev03cc <- feols(uer ~ active_mines + lag_mines +lag_mines2 + lag_mines3 + log_realgdp_pc | 
               fips + year, allcomp_cc, se = 'twoway')
lev02cc <- feols(uer ~ active_mines + lag_mines +lag_mines2 + log_realgdp_pc | 
               fips + year, allcomp_cc, se = 'twoway')
etable(lev03, lev22, lev33, lev03cc, lev22cc, lev33cc, order = c("active_mines,3", "active_mines,2", "active_mines,1", "Active Mines", "log_realgdp_pc"))
# Level mines and log emp indicators
lev_emplog <- feols(log_employed ~ active_mines + lag_mines + lag_mines2 + log_realgdp + log_pop | 
               fips + year, allcomp, se = 'twoway')
lev_unemplog <- feols(log_unemployed ~ active_mines + lag_mines + lag_mines2 + log_realgdp + log_pop | 
               fips + year, allcomp, se = 'twoway')
lev_lflog <- feols(log_lf ~ active_mines + lag_mines + lag_mines2 + log_realgdp + log_pop| 
               fips + year, allcomp, se = 'twoway')
lev_poplog <- feols(log_pop ~ active_mines + lag_mines + lag_mines2 + log_realgdp | 
               fips + year, allcomp, se = 'twoway')

etable(lev02, lev_emplog, lev_unemplog, lev_lflog, lev_poplog)
# Levels of everything
lev_emp <- feols(employed ~ active_mines + lag_mines + lag_mines2 + log_realgdp + log_pop | 
               fips + year, allcomp, se = 'twoway')
lev_unemp <- feols(unemployed ~ active_mines + lag_mines + lag_mines2 + log_realgdp + log_pop | 
               fips + year, allcomp, se = 'twoway')
lev_lf <- feols(labour_force ~ active_mines + lag_mines + lag_mines2 + log_realgdp + log_pop | 
               fips + year, allcomp, se = 'twoway')
lev_pop <- feols(pop ~ active_mines + lag_mines + lag_mines2 + log_realgdp | 
               fips + year, allcomp, se = 'twoway')
etable(lev_emp, lev_unemp, lev_lf, lev_pop)
# log dependent variables first-difference independent variables

uer_diff <- feols(uer ~ mines_diff + lag_diff + lag_diff2 + log_realgdp_pc | 
               fips + year, allcomp, se = 'twoway')
log_emp <- feols(log_employed ~ mines_diff + lag_diff + lag_diff2 + log_realgdp + log_pop | 
               fips + year, allcomp, se = 'twoway')

log_unemp <- feols(log_unemployed ~ mines_diff + lag_diff + lag_diff2 + log_realgdp + log_pop | 
               fips + year, allcomp, se = 'twoway')
log_lf <- feols(log_lf ~ mines_diff + lag_diff + lag_diff2 + log_realgdp + log_pop | 
               fips + year, allcomp, se = 'twoway')
log_pop <- feols(log_pop ~ mines_diff + lag_diff + lag_diff2 + log_realgdp | 
               fips + year, allcomp, se = 'twoway')
etable(uer_diff, log_emp, log_unemp, log_lf, log_pop)

2 Regressions without combined time periods

The coefficient estimates are the same in each of these models isolating each variable.

etable("t" = feols(diff_uer ~ mines_diff + diff_log_realgdp_pc | fips + year, allcomp, se = 'twoway'),
       "t-1" = feols(diff_uer ~ lag_diff + diff_log_realgdp_pc | fips + year, allcomp, se = 'twoway'),
       "t-2" = feols(diff_uer ~ lag_diff2 + diff_log_realgdp_pc | fips + year, allcomp, se = 'twoway'),
       "t-3" = feols(diff_uer ~ lag_diff3 + diff_log_realgdp_pc | fips + year, allcomp, se = 'twoway'),
       "t" = feols(diff_uer ~ mines_diff + diff_log_realgdp_pc | fips + year, allcomp_cc, se = 'twoway'),
       "t-1" = feols(diff_uer ~ lag_diff + diff_log_realgdp_pc | fips + year, allcomp_cc, se = 'twoway'),
       "t-2" = feols(diff_uer ~ lag_diff2 + diff_log_realgdp_pc | fips + year, allcomp_cc, se = 'twoway'),
       "t-3" = feols(diff_uer ~ lag_diff3 + diff_log_realgdp_pc | fips + year, allcomp_cc, se = 'twoway'),tex= TRUE,
       signifCode = c(`***` = 0.001, `**` = 0.01, `*` = 0.05, . = 0.1))
\begin{tabular}{lcccccccc}
   \tabularnewline\midrule\midrule
   Dependent Variable: & \multicolumn{8}{c}{Change Unemployment Rate}\\
   Model:                      & (1)             & (2)             & (3)             & (4)             & (5)            & (6)            & (7)            & (8)\\
   \midrule \emph{Variables} &   &   &   &   &   &   &   &  \\
   Change Active Mines         & -0.0560$^{**}$  &                 &                 &                 & -0.0416$^{**}$ &                &                &   \\
                               & (0.0151)        &                 &                 &                 & (0.0127)       &                &                &   \\
   Change in (log) Real GDP pc & -0.9742$^{***}$ & -0.9837$^{***}$ & -0.9846$^{***}$ & -0.9842$^{***}$ & -1.596$^{***}$ & -1.699$^{***}$ & -1.701$^{***}$ & -1.702$^{***}$\\
                               & (0.2137)        & (0.2137)        & (0.2138)        & (0.2136)        & (0.3513)       & (0.3482)       & (0.3416)       & (0.3469)\\
   Change Active Mines (t-1)   &                 & -0.0050         &                 &                 &                & -0.0029        &                &   \\
                               &                 & (0.0137)        &                 &                 &                & (0.0096)       &                &   \\
   Change Active Mines (t-2)   &                 &                 & 0.0402$^{*}$    &                 &                &                & 0.0351$^{**}$  &   \\
                               &                 &                 & (0.0151)        &                 &                &                & (0.0116)       &   \\
   Change Active Mines (t-3)   &                 &                 &                 & 0.0011          &                &                &                & 0.0026\\
                               &                 &                 &                 & (0.0136)        &                &                &                & (0.0098)\\
   \midrule \emph{Fixed-effects} &   &   &   &   &   &   &   &  \\
   County FIPS Code            & Yes             & Yes             & Yes             & Yes             & Yes            & Yes            & Yes            & Yes\\
   Year                        & Yes             & Yes             & Yes             & Yes             & Yes            & Yes            & Yes            & Yes\\
   \midrule \emph{Fit statistics} &   &   &   &   &   &   &   &  \\
   Observations                & 55,295          & 55,295          & 55,295          & 55,295          & 4,518          & 4,518          & 4,518          & 4,518\\
   R$^2$                       & 0.61298         & 0.61227         & 0.61267         & 0.61226         & 0.65184        & 0.64795        & 0.65105        & 0.64794\\
   Within R$^2$                & 0.01367         & 0.01186         & 0.01288         & 0.01185         & 0.03705        & 0.02628        & 0.03487        & 0.02627\\
   \midrule\midrule\multicolumn{9}{l}{\emph{Clustered (County FIPS Code \& Year) standard-errors in parentheses}}\\
   \multicolumn{9}{l}{\emph{Signif. Codes: ***: 0.001, **: 0.01, *: 0.05, .: 0.1}}\\
\end{tabular}
# Regresses the change in unemployment rate, employed person, unemployed 
# persons, labour force, population on change in mines and two time lags using 
# full set of 3,079 contiguous US counties. 

FE_diffuer <- as.formula("diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc | fips + year")
FE_negdiffuer <- as.formula("neg_diffuer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc | fips + year")
FE_negdiffuer3 <- as.formula("neg_diffuer ~ mines_diff + lag_diff + lag_diff2 +lag_diff3 + diff_log_realgdp_pc | fips + year")

es <- feols(FE_negdiffuer, allcomp_full, panel.id = ~fips+year, se = 'twoway')
es_cc <- feols(FE_negdiffuer, allcomp_cc, panel.id = ~fips+year, se = 'twoway')
es3 <- feols(FE_negdiffuer3, allcomp_full, panel.id = ~fips+year, se = 'twoway')
es3_cc <- feols(FE_negdiffuer3, allcomp_cc, panel.id = ~fips+year, se = 'twoway')
es_2neg <- feols(neg_diffuer ~ l(mines_diff, -2:0) + lag_diff + lag_diff2 + diff_log_realgdp_pc | 
               fips + year, allcomp_full, panel.id = ~fips+year, se = 'twoway')
es_3neg <- feols(neg_diffuer ~ l(mines_diff, -3:0) + lag_diff + lag_diff2 + lag_diff3 + diff_log_realgdp_pc | 
               fips + year, allcomp_full, panel.id = ~fips+year, se = 'twoway')
es_2negcc <- feols(neg_diffuer ~ l(mines_diff, -2:0) + lag_diff + lag_diff2 + diff_log_realgdp_pc | 
               fips + year, allcomp_cc, panel.id = ~fips+year, se = 'twoway')
es_3negcc <- feols(neg_diffuer ~ l(mines_diff, -3:0) + lag_diff + lag_diff2 + lag_diff3 + diff_log_realgdp_pc | 
               fips + year, allcomp_cc, panel.id = ~fips+year, se = 'twoway')

etable(es, es_cc, es3, es3_cc, es_2neg, es_2negcc, es_3neg, es_3negcc)
fill_colors <- c(NA, "#39568CFF", "#95D840FF")
line_colors <- c(NA, "#440154FF", "#287D8EFF")
setFixest_coefplot(pt.join = TRUE, ci.join = FALSE, ci.fill = TRUE, ci.col = fill_colors, 
                   pt.col = line_colors, pt.pch = c(0, 19, 25), pt.bg = line_colors,
                   pt.cex = 1, ci.fill.par = list(col = fill_colors, alpha = 0.3), 
                   ci.lty = "dashed", ref.line = TRUE, ref.line.par = list(v = 4, col = "gray", lty = "solid"),
                   grid = TRUE, grid.par = list(vert = FALSE, lwd = 1), dict = plot_dict)

es_blank <- feols(diff_uer ~ l(mines_diff, -3:0) + lag_diff + lag_diff2 + lag_diff3 + diff_log_realgdp_pc | 
               fips + year, allcomp_cc, panel.id = ~fips+year, se = 'twoway')

coefplot(list(es_3negcc, es3, es3_cc), drop = c('diff_log_realgdp_pc'), xlim.add = c(0,0.001))

coefplot(list(es_blank, es_2neg, es_2negcc), drop = c('diff_log_realgdp_pc'), xlim.add = c(0,0.06))

coefplot(list(es_blank, es_3neg, es_3negcc), drop = c('diff_log_realgdp_pc'), xlab = "Years since negative change in active mines")

plot(NULL ,xaxt='n',yaxt='n',bty='n',ylab='',xlab='', xlim=0:1, ylim=0:1)
legend("topleft", legend = c("US Counties", "Coal Counties Only", "95% Confidence Interval", "95% Confidence Interval"), 
  col = c("#440154FF", "#287D8EFF", "#39568C4D", "#95D8404D"), 
  pt.bg = c("#440154FF", "#287D8EFF", NA, NA),
  pch = c(19,25,15,15),
  bty = "n", 
  pt.cex = c(1.5,1.5,3.5,3.5), 
  cex = 1, 
  text.col = "black", 
  ncol = 2,
  lty = c("solid", "solid", NA, NA),
  y.intersp = 1.5
  )

2.0.1 Change in active mines interacted with rural (1) vs. urban (0)

Research Question: Are changes in the amount of active mines associated with changes in the unemployment rate, conditional on whether a county is rural or not? Ie. do changes in active mines impact rural and urban counties differently?

Results:

  1. Models 6: Including rural = 1 interaction term does not add much information. Likely that an already small unemployment rate change disaggregated by county type weakens ability to detect an impact?
# First (second) model uses set of all US counties (subset of coal counties only).

etable("Model 6_i" = feols(diff_uer ~ mines_diff + lag_diff + lag_diff2 +  mines_diff:ruc_bin + lag_diff:ruc_bin + lag_diff2:ruc_bin + diff_log_realgdp_pc | 
               fips + year, allcomp, se = 'twoway'),
       "Model 6_i CC" = feols(diff_uer ~ mines_diff + lag_diff + lag_diff2 +  mines_diff:ruc_bin + lag_diff:ruc_bin + lag_diff2:ruc_bin + diff_log_realgdp_pc  | 
               fips + year, allcomp_cc, se = 'twoway'))

2.0.2 Asymmetric treatment attempt

Research Question: What is the impact of a positive (negative) change in active mines on county-level unemployment rate?

The point estimates indicate a potential asymmetric treatment (mine closures lead to a larger change in unemployment rate as compared to the opening of a mine) effect but is not statistically significant.

Potential interesting result shows that the coefficient on change in mines is restricted to NEGATIVE changes. Essentially, this evidence shows that positive changes in active mines do not provide “windfall” employment, indicating that the solution is not to RETAIN or re-invigorate the mining sector.

main <- feols(FE_diffuer, allcomp, se = 'twoway')
main_cc <- feols(FE_diffuer, allcomp_cc, se = 'twoway')
allcomp$pos_difflag <- ifelse(allcomp$lag_diff >= 0, 1, 0)
allcomp$pos_difflag2 <- ifelse(allcomp$lag_diff2 >= 0, 1, 0)
allcomp$neg_difflag <- ifelse(allcomp$lag_diff < 0, 1, 0)
allcomp$neg_difflag2 <- ifelse(allcomp$lag_diff2 < 0, 1, 0)

# Uses binary indicators for whether a change in active mines was positive (or zero) 
# versus negative.
# Below uses set of all contiguous US counties
model_7_neg= feols(diff_uer ~ mines_diff + lag_diff + lag_diff2 + neg_diff:mines_diff + 
                               neg_difflag:lag_diff + neg_difflag2:lag_diff2 + diff_log_realgdp_pc | 
                               fips + year, allcomp, se = 'twoway')

model_8_pos = feols(diff_uer ~ mines_diff + lag_diff +lag_diff2 + pos_diff:mines_diff + 
                               pos_difflag:lag_diff + pos_difflag2:lag_diff2 + diff_log_realgdp_pc | 
                            fips + year, allcomp, se = 'twoway')

model_9_both = feols(diff_uer ~ neg_diff:mines_diff + neg_difflag:lag_diff + neg_difflag2:lag_diff2 + pos_diff:mines_diff + pos_difflag:lag_diff + pos_difflag2:lag_diff2 + diff_log_realgdp_pc | fips + year, allcomp, se = 'twoway')

etable(model_7_neg, model_8_pos, model_9_both, signifCode = c(`***` = 0.001, `**` = 0.01, `*` = 0.05, . = 0.1))
etable(model_9_both)
# Mines_diff is different from zero (statistically significant at the .1% level) (-0.056%)
glht(main, linfct = "mines_diff  = 0") %>% summary

     Simultaneous Tests for General Linear Hypotheses

Fit: feols(fml = FE_diffuer, data = allcomp, se = "twoway")

Linear Hypotheses:
                Estimate Std. Error t value Pr(>|t|)    
mines_diff == 0 -0.05630    0.01408  -3.998  6.4e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Adjusted p values reported -- single-step method)
glht(main, linfct = "mines_diff + lag_diff + lag_diff2 = 0") %>% summary

     Simultaneous Tests for General Linear Hypotheses

Fit: feols(fml = FE_diffuer, data = allcomp, se = "twoway")

Linear Hypotheses:
                                       Estimate Std. Error t value Pr(>|t|)
mines_diff + lag_diff + lag_diff2 == 0 -0.02443    0.02690  -0.908    0.364
(Adjusted p values reported -- single-step method)
#glht(main, linfct = mcp(tension = c("mines_diff  = 0", "mines_diff + lag_diff + lag_diff2 = 0")))

# Mines_diff + mines_diff:neg_diff is different from zero (statistically significant at the 1% level)
# Higher coefficient value than in broader model (-0.078%)
glht(model_7_neg, linfct = "mines_diff + mines_diff:neg_diff = 0") %>% summary

     Simultaneous Tests for General Linear Hypotheses

Fit: feols(fml = diff_uer ~ mines_diff + lag_diff + lag_diff2 + neg_diff:mines_diff + 
    neg_difflag:lag_diff + neg_difflag2:lag_diff2 + diff_log_realgdp_pc | 
    fips + year, data = allcomp, se = "twoway")

Linear Hypotheses:
                                      Estimate Std. Error t value Pr(>|t|)   
mines_diff + mines_diff:neg_diff == 0 -0.07520    0.02346  -3.206  0.00135 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Adjusted p values reported -- single-step method)
glht(model_7_neg, linfct = "mines_diff + mines_diff:neg_diff + lag_diff + lag_diff:neg_difflag + lag_diff2 + lag_diff2:neg_difflag2 = 0") %>% summary

     Simultaneous Tests for General Linear Hypotheses

Fit: feols(fml = diff_uer ~ mines_diff + lag_diff + lag_diff2 + neg_diff:mines_diff + 
    neg_difflag:lag_diff + neg_difflag2:lag_diff2 + diff_log_realgdp_pc | 
    fips + year, data = allcomp, se = "twoway")

Linear Hypotheses:
                                                                                                             Estimate
mines_diff + mines_diff:neg_diff + lag_diff + lag_diff:neg_difflag + lag_diff2 + lag_diff2:neg_difflag2 == 0 -0.05065
                                                                                                             Std. Error
mines_diff + mines_diff:neg_diff + lag_diff + lag_diff:neg_difflag + lag_diff2 + lag_diff2:neg_difflag2 == 0    0.03093
                                                                                                             t value
mines_diff + mines_diff:neg_diff + lag_diff + lag_diff:neg_difflag + lag_diff2 + lag_diff2:neg_difflag2 == 0  -1.638
                                                                                                             Pr(>|t|)
mines_diff + mines_diff:neg_diff + lag_diff + lag_diff:neg_difflag + lag_diff2 + lag_diff2:neg_difflag2 == 0    0.101
(Adjusted p values reported -- single-step method)
# Mines_diff and pos_diff is different from zero although not statistically significant and at a much lower magnitude! (-0.021%)
glht(model_8_pos, linfct = "mines_diff + mines_diff:pos_diff = 0") %>% summary

     Simultaneous Tests for General Linear Hypotheses

Fit: feols(fml = diff_uer ~ mines_diff + lag_diff + lag_diff2 + pos_diff:mines_diff + 
    pos_difflag:lag_diff + pos_difflag2:lag_diff2 + diff_log_realgdp_pc | 
    fips + year, data = allcomp, se = "twoway")

Linear Hypotheses:
                                      Estimate Std. Error t value Pr(>|t|)
mines_diff + mines_diff:pos_diff == 0 -0.01931    0.02006  -0.962    0.336
(Adjusted p values reported -- single-step method)
glht(model_8_pos, linfct = "mines_diff + mines_diff:pos_diff + lag_diff + lag_diff:pos_difflag + lag_diff2 + lag_diff2:pos_difflag2 = 0") %>% summary

     Simultaneous Tests for General Linear Hypotheses

Fit: feols(fml = diff_uer ~ mines_diff + lag_diff + lag_diff2 + pos_diff:mines_diff + 
    pos_difflag:lag_diff + pos_difflag2:lag_diff2 + diff_log_realgdp_pc | 
    fips + year, data = allcomp, se = "twoway")

Linear Hypotheses:
                                                                                                             Estimate
mines_diff + mines_diff:pos_diff + lag_diff + lag_diff:pos_difflag + lag_diff2 + lag_diff2:pos_difflag2 == 0  0.01170
                                                                                                             Std. Error
mines_diff + mines_diff:pos_diff + lag_diff + lag_diff:pos_difflag + lag_diff2 + lag_diff2:pos_difflag2 == 0    0.05724
                                                                                                             t value
mines_diff + mines_diff:pos_diff + lag_diff + lag_diff:pos_difflag + lag_diff2 + lag_diff2:pos_difflag2 == 0   0.204
                                                                                                             Pr(>|t|)
mines_diff + mines_diff:pos_diff + lag_diff + lag_diff:pos_difflag + lag_diff2 + lag_diff2:pos_difflag2 == 0    0.838
(Adjusted p values reported -- single-step method)

2.0.3 Incorporating county types

The typology work is not included in this set of results. The following regressions take the set of 251 coal counties (as defined above) and subset into three groups depending on whether they are Type 1, 2, or 3 counties (see summary table in paper draft).

Results:

  1. Main result is from CC Rural Model. Shows that the only type of county with statistically significant impacts from changes in active mines is the Type 1 rural, low-income, smaller, lower percentage of college education, lower female LFPR, etc. The effect detected here follows the same behavior as that in Model 1 and Model 1 CC.
main <- feols(FE_diffuer, allcomp, se = 'twoway')
main_cc <- feols(FE_diffuer, allcomp_cc, se = 'twoway')

##### Run regressions from previous section incorporating the type of county. ########
# The county types (1,2,3) are derived from the clustering typology exercise outlined in the paper draft.
# Types are as follows: (rural:1; medium:2, urban:3)

cc_urbandf <- allcomp_cc_types %>% subset(type == 1)
cc_mediumdf <- allcomp_cc_types %>% subset(type == 2)
cc_ruraldf <- allcomp_cc_types %>% subset(type == 3)

cc_rural = feols(FE_diffuer, cc_ruraldf, se = "twoway")
cc_medium = feols(FE_diffuer, cc_mediumdf, se = "twoway")
cc_urban = feols(FE_diffuer, cc_urbandf, se = "twoway")

rural_2neg <- feols(diff_uer ~ l(mines_diff, -1:0) + lag_diff + lag_diff2 + lag_diff3 + diff_log_realgdp_pc | 
               fips + year, cc_ruraldf, panel.id = ~fips+year, se = 'twoway')
medium_2neg <- feols(diff_uer ~ l(mines_diff, -1:0) + lag_diff + lag_diff2 + lag_diff3 + diff_log_realgdp_pc | 
               fips + year, cc_mediumdf, panel.id = ~fips+year, se = 'twoway')
urban_2neg <- feols(diff_uer ~ l(mines_diff, -1:0) + lag_diff + lag_diff2 + lag_diff3 +diff_log_realgdp_pc | 
               fips + year, cc_urbandf, panel.id = ~fips+year, se = 'twoway')


fill_colors <- c("#39568CFF", "#95D840FF", "#eb7134")
line_colors <- c("#440154FF", "#287D8EFF", "#eb7134")
setFixest_coefplot(pt.join = TRUE, pt.join.par = list(lwd = 2, lty = c("solid", "dashed", "dashed")), 
                   ci.join = TRUE, ci.join.par = list(lty = c("dashed", "blank", "blank"), lwd = 0.5, col = line_colors), ci.fill = TRUE, ci.col = fill_colors, 
                   pt.col = line_colors, pt.pch = c(25, 23, 19), pt.bg = line_colors,
                   pt.cex = 1, ci.fill.par = list(col = fill_colors, alpha = 0.1), ci.lwd = 0.5, 
                   ci.lty = "blank", ref.line = TRUE, ref.line.par = list(v = 2, col = "gray", lty = "solid"),
                   grid = TRUE, grid.par = list(vert = FALSE, lwd = 1), dict = plot_dict, sep = 0)

coefplot(list(rural_2neg, medium_2neg, urban_2neg),drop = c('diff_log_realgdp_pc'), xlab = "Years since negative change in active mines")
legend("topright", legend = c("Type 1", "Type 2", "Type 3"), col = c( "#eb7134","#287D8EFF","#440154FF"), pt.bg= c( "#eb7134","#287D8EFF","#440154FF"), pch = c(19, 23, 25), cex = 1, 
  text.col = "black", lty = c("dashed", "dashed", "solid"))

# coefplot(list(rural_2neg),drop = c('diff_log_realgdp_pc'))
# coefplot(list(medium_2neg),drop = c('diff_log_realgdp_pc'))
# coefplot(list(urban_2neg),drop = c('diff_log_realgdp_pc'))

etable("CC Urban" = cc_urban, "CC Medium" = cc_medium, "CC Rural" = cc_rural, urban_2neg, medium_2neg, rural_2neg, order = c("mines_diff,1", "Change Active Mines"))
 model_types <- list(
  "Rural Type 1 Counties" = cc_rural,
  "Medium Type 2 Counties" = cc_medium,
  "Urban Type 3 Counties" = cc_urban)

 
test <- allcomp_cc_types %>% 
  group_by(fips) %>% 
  summarize(m1 = length(unique(mines_diff)), m1mean = mean(mines_diff),
            m2 = length(unique(lag_diff)), m2mean = mean(lag_diff),
            m3 = length(unique(lag_diff2)), m3mean = mean(lag_diff2))

test2 <- subset(test, (m1 == 1 & m1mean == 0) | (m2 == 1 & m2mean == 0) | (m3 == 1 & m3mean == 0))
const_fips <- unique(test2$fips)
allcomp_pdmif <- subset(allcomp_cc_types, !(fips %in% const_fips))
filter(allcomp_cc_types, fips %in% const_fips) %>% 
  group_by(fips) %>% 
  summarize(mean = mean(active_mines), max = max(active_mines), min = min(active_mines))
allcomp_pdmif %>% 
  group_by(fips) %>% 
  summarize(mean = mean(active_mines), max = max(active_mines), min = min(active_mines))
etable("REE Model Rural" = feols(diff_uer ~ mines_diff + lag_diff + lag_diff2 + mines_diff:REE_bin_top90 +
                             lag_diff:REE_bin_top90 + lag_diff2:REE_bin_top90 + REE_bin_top90 + diff_log_realgdp_pc | fips + year, cc_ruraldf,  se = 'twoway'),
"REE Model Medium" = feols(diff_uer ~ mines_diff + lag_diff + lag_diff2 + mines_diff:REE_bin_top90 +
                             lag_diff:REE_bin_top90 + lag_diff2:REE_bin_top90 + REE_bin_top90 + diff_log_realgdp_pc | fips + year, cc_mediumdf,  se = 'twoway'),
            "REE Model Urban" = feols(diff_uer ~ mines_diff + lag_diff + lag_diff2 + mines_diff:REE_bin_top90 +
                             lag_diff:REE_bin_top90 + lag_diff2:REE_bin_top90 + REE_bin_top90 + diff_log_realgdp_pc | fips + year, cc_urbandf,  se = 'twoway'))
## Incorporating county types using PDMIF
# X can only have p columns where p = explanatory variables.

data4x <- select(allcomp_pdmif, c(mines_diff, lag_diff, lag_diff2, diff_log_realgdp_pc))
test <- as.matrix(data4x)
data4y <- select(allcomp_pdmif, c(year, fips, diff_uer))
yrs <- as.list(unique(data4y$year))
fps <- as.list(unique(data4y$fips))
data4y <- matrix(data = data4y$diff_uer, 18, 236, dimnames = list(yrs, fps))
groupmem <- allcomp_pdmif %>%
  group_by(fips) %>%
  summarize(type = unique(type))
groupmem <- data.frame(groupmem[,-1], row.names = groupmem$fips)

pdmif <- PDMIFLING(X = test, Y = data4y, Membership = groupmem, NGfactors = 1, NLfactors = c(2,2,2), Maxit = 30, tol = 0.01)

HYPTEST(pdmif$Coefficients,data.frame(c(0,1),c(-1,2)),pdmif$Se,"two",c(1,3),c(1,2))
dim(pdmif$Coefficients)
[1]   5 236
length(pdmif$GlobalFactors)
[1] 18
dim(pdmif$GlobalLoadings)
[1] 236   1
dim(pdmif$GroupFactors)
[1] 18  6
pdmif$GroupLoadings
             [,1]          [,2]
  [1,] -1.7159587 -0.4970947242
  [2,] -1.2506511 -0.1210225646
  [3,] -1.3852535  0.0951779712
  [4,] -1.6891283 -0.3700719870
  [5,] -1.4116762 -0.2431827634
  [6,] -1.5889026 -0.4369745256
  [7,] -1.1397298 -0.1113813290
  [8,] -1.7720693 -0.8481169382
  [9,] -0.6990435 -0.1085787023
 [10,] -1.1493175 -0.1049787054
 [11,] -1.2850767  0.2441902281
 [12,] -1.8289951 -0.0551859601
 [13,] -1.4889412 -0.3418151961
 [14,] -0.6844750  0.1285262330
 [15,] -1.2232881  0.7003147640
 [16,] -1.3245457  0.6977057578
 [17,] -0.7053269  0.2276478006
 [18,] -0.8012127  0.5346583256
 [19,] -1.0619985  0.4226133227
 [20,] -0.8184010  0.5107081237
 [21,] -0.9289887  0.9545144094
 [22,] -1.1570968  0.5137473310
 [23,] -0.9022382 -0.1841984663
 [24,] -0.8561709  0.0529724582
 [25,] -0.8572588  0.0640358643
 [26,] -0.6193238  0.1655374527
 [27,] -0.9816035  0.1722331877
 [28,] -0.9247881  0.0888869389
 [29,] -1.0104618 -0.0160600749
 [30,] -1.0653686  0.0623523863
 [31,] -0.7243818  0.0205224419
 [32,] -0.8214763 -0.1037216678
 [33,] -0.9651488  0.3441734817
 [34,] -0.7814670  0.1857697878
 [35,] -0.9546420  0.3345663967
 [36,] -1.2289414  0.1307069765
 [37,] -1.1715969 -0.0506078405
 [38,] -0.7134562 -0.0917209584
 [39,] -0.7213129  0.0353784193
 [40,] -0.6562196  0.1956879895
 [41,] -1.1195242 -0.1118518538
 [42,] -1.5049287 -0.3402423394
 [43,] -0.5950205 -0.0524152399
 [44,] -0.9564424 -0.2097201766
 [45,] -0.8805123 -0.1802990870
 [46,] -0.7781600  0.2327846675
 [47,] -0.6106139  0.0233912923
 [48,] -1.0577448 -0.3639123495
 [49,] -1.2064187 -0.1095127000
 [50,] -1.1068908 -0.3172667473
 [51,] -0.9374111 -0.0133463758
 [52,] -1.1961704  0.1398445108
 [53,] -0.9696961 -0.0486858213
 [54,] -0.6986131  0.3401840296
 [55,] -0.8987623 -0.1034980341
 [56,] -0.9251594 -0.3210185549
 [57,] -1.1128281  0.2323201823
 [58,] -0.9113787  0.3688966824
 [59,] -1.3141056  0.5395957302
 [60,] -1.0405316 -0.5282643749
 [61,] -1.1888027 -0.5093174525
 [62,] -0.9186122  1.0098312393
 [63,] -1.0380936 -0.2482277838
 [64,] -1.4471845 -0.5781267395
 [65,] -1.1753829 -0.8862518737
 [66,] -1.4872084  0.3061709516
 [67,] -1.4529820  1.5516485033
 [68,] -1.3093274 -0.3022461181
 [69,] -0.8658539  0.0899705748
 [70,] -0.8516251 -1.0527058808
 [71,] -1.1340828 -0.0512920666
 [72,] -1.7264298  0.2594457432
 [73,] -1.2419140 -0.0519711858
 [74,] -1.2411303  0.1354792515
 [75,] -1.3259565 -0.3728491504
 [76,] -1.4557448  0.0996240563
 [77,] -1.7489861  0.7272107918
 [78,] -1.8991391  1.2358124153
 [79,] -1.1642256  0.3255957086
 [80,] -1.1360085 -0.4995905667
 [81,] -0.9777500 -0.0469936368
 [82,] -3.2231074 -0.8893175141
 [83,] -1.2205143 -0.3493537736
 [84,] -1.0263532  0.4138303958
 [85,] -0.9969444 -0.2351800257
 [86,] -0.6030036 -0.3112774989
 [87,] -1.3483443  0.1360292072
 [88,] -1.0176361  0.0896070103
 [89,] -1.1461173  0.4158326830
 [90,] -1.3666393  0.1940533039
 [91,] -0.9136789 -0.0002519597
 [92,] -1.4476817 -0.8506894706
 [93,] -1.3814870 -0.5980382220
 [94,] -1.0589382  0.0833802672
 [95,] -1.3031605  0.2013379285
 [96,] -1.2928716 -0.3700868363
 [97,] -0.8279977  0.0585134491
 [98,] -0.8785844 -0.1431073276
 [99,] -0.7823444 -0.0862205039
[100,] -0.8280243  0.0434538528
[101,] -1.0104990 -0.3956392895
[102,] -0.8944580 -0.0386228796
[103,] -0.9631695 -0.4234373064
[104,] -0.6130952 -0.2180252940
[105,] -0.8771488  0.5007586463
[106,] -0.9587444  0.4525580387
[107,] -0.3352550 -0.0399822978
[108,] -0.7878129  0.2681974845
[109,] -1.3692738  0.9954848539
[110,] -0.0388166  0.2325314582
[111,] -0.1619084  0.3432594547
[112,] -0.7494835  0.1539595032
[113,] -1.0347479  0.4739020803
[114,] -1.8120357 -0.0546686536
[115,] -1.6301879 -0.1424868413
[116,] -1.3106365 -0.1925992150
[117,] -0.7532339  0.6658529224
[118,] -1.4021690  0.2698509785
[119,] -1.6203980  0.4930464337
[120,] -1.1000921  0.2634289591
[121,] -1.4821608  0.4766755818
[122,] -0.8299076  0.5209184678
[123,] -1.3394871 -0.4322651178
[124,] -1.2797990  0.2210591342
[125,] -1.1508171 -0.0579231206
[126,] -1.6090337 -0.6913765937
[127,] -1.3921306 -0.1400417597
[128,] -1.2632700  0.2824218574
[129,] -1.5144344 -0.4241526262
[130,] -1.5008877  0.0276889948
[131,] -1.2386612  0.2194629341
[132,] -0.6138597  0.2479686786
[133,] -1.1809869  0.3918627056
[134,] -1.6450132 -0.3970821792
[135,] -1.0576647 -0.2042638377
[136,] -1.2260013 -0.1215535591
[137,] -1.1586916 -0.3999808763
[138,] -0.5857067  0.0586684273
[139,] -0.8277376 -0.1253250798
[140,] -0.6934433  0.0245373964
[141,] -1.0154204 -0.3166618409
[142,] -0.9516930 -0.1234327292
[143,] -0.5787977  0.0871874729
[144,] -0.6857673 -0.0865126044
[145,] -0.6726116  0.0908316511
[146,] -2.0236565 -1.4671095980
[147,] -0.8185153  0.1059080234
[148,] -0.4445917 -0.0390567038
[149,] -0.7707123  0.1322442020
[150,] -0.9103312 -0.0188514287
[151,] -0.7636032  0.1064530310
[152,] -0.6773481  0.0593553275
[153,] -1.4142426 -0.8555154621
[154,] -0.8215154  0.1441490675
[155,] -0.6624739  0.3986174726
[156,] -1.2639486 -0.4639307854
[157,] -0.6616146  0.1237445651
[158,] -0.9684697  0.0344129733
[159,] -0.7139272  0.0920895803
[160,] -0.7711334 -0.2856983587
[161,] -0.7738742  0.1430025575
[162,] -0.8066399 -0.1356472919
[163,] -0.9938972 -0.3934373704
[164,] -0.7655618  0.0535409956
[165,] -0.9513507  0.0590745474
[166,] -0.6140954  0.3044040558
[167,] -0.6521045 -0.3781353307
[168,] -0.6930063 -0.0201498550
[169,] -0.7507337 -0.1291831619
[170,] -0.6990263 -0.0612840621
[171,] -1.1002929 -0.2377199046
[172,] -1.3468597 -0.0484495258
[173,] -1.1441332 -0.2970481079
[174,] -0.7442754 -0.1115915965
[175,] -0.9590780 -0.2385965480
[176,] -0.8431418 -0.4488037712
[177,] -1.2421905 -0.2888702275
[178,] -2.1858810 -0.2374365836
[179,] -0.8111338 -0.0612775264
[180,] -1.0690627  0.2877466455
[181,] -0.6426882  0.4491948858
[182,] -0.8435226  0.2739970445
[183,] -1.0222202  0.5328307516
[184,] -0.5689554  0.4860229695
[185,] -1.1786594  0.3750612466
[186,] -1.5089804  0.0453876036
[187,] -1.1257778  0.4533350675
[188,] -1.0162773  0.1559590823
[189,] -0.8766348  0.3469732463
[190,] -0.9210683 -0.0344189592
[191,] -0.8644603  0.2006912723
[192,] -0.9583652  0.1162274757
[193,] -0.9119081  0.0924023410
[194,] -1.2247518  0.0927640938
[195,] -1.2078894  0.4021836572
[196,] -1.2895472  0.2569593215
[197,] -0.9936167 -0.0908636937
[198,] -0.6031454  0.1485510045
[199,] -0.9435497 -0.4717026699
[200,] -0.8502963  0.3166791492
[201,] -0.7801652  0.6673449513
[202,] -0.9853939 -0.0850541220
[203,] -1.1018199  0.1125817476
[204,] -1.1959980  0.2889242063
[205,] -1.0319688 -0.5079036935
[206,] -1.4556056 -0.1961404224
[207,] -1.1004810  0.2789980436
[208,] -1.4615248 -0.1154133489
[209,] -1.1468907 -0.3616324474
[210,] -0.5556949  0.1523339070
[211,] -0.8472345  0.1424326726
[212,] -1.4460356 -0.2866694135
[213,] -1.4032476  0.3269551179
[214,] -1.4665441  0.0575400029
[215,] -0.7552432  0.1326445954
[216,] -1.1412369  0.1002969852
[217,] -1.1462284 -0.5101252567
[218,] -0.6988479  0.4242532034
[219,] -0.6276576  0.2000978373
[220,] -1.5269998  0.5727156333
[221,] -0.4989364  0.2012265103
[222,] -1.1288666  0.3383150708
[223,] -1.0414479 -0.0198865100
[224,] -0.9138263  0.2040288935
[225,] -0.8895445  0.2033318353
[226,] -1.0154902  0.0445392741
[227,] -0.7431987 -0.0300771678
[228,] -1.3694650 -0.3693082574
[229,] -1.2409798  0.0174563608
[230,] -0.7850017 -0.3177054613
[231,] -1.2863975 -0.1015976608
[232,] -1.2722144  0.1618367362
[233,] -0.7796648  0.6137700202
[234,] -0.8196192 -0.0269958812
[235,] -0.6506832 -0.0254960712
[236,] -1.1407800  0.2121138127
pdmif$Coefficients
             [,1]        [,2]         [,3]        [,4]         [,5]        [,6]
[1,] -0.096982562  0.01581241  0.004484555 -0.06428478  -0.06457646 -0.08994161
[2,]  0.197465619  0.30025959  0.227363599  1.33537524   0.21310697 -0.22952988
[3,]  0.172749716 -0.30190028 -0.249475015 -0.69659703  -0.22768456 -0.21320294
[4,] -0.006667603  0.21835560  0.019699628  0.70605687  -0.31344583 -0.15774282
[5,]  2.182062635 -4.56205117 -4.047812105  1.79568525 -10.40618262  2.03012763
            [,7]          [,8]         [,9]        [,10]      [,11]
[1,] -0.02181789  -0.081344811 -0.003662305 -0.002339964 0.01698375
[2,]  0.02865299  -0.070730460  0.038953983  0.108925737 0.06218569
[3,] -0.05895855  -0.073898643  0.090026729 -0.093408741 0.07307387
[4,] -0.03622374   0.005365252  0.019108257  0.007243005 0.20864046
[5,] -7.20190818 -10.564478500 -2.982206286 -5.797698275 0.26856310
            [,12]     [,13]       [,14]      [,15]      [,16]      [,17]
[1,]  -0.06946269 0.2736765 -0.02650192 0.09578466  0.1671813 0.05739564
[2,]   0.25444949 0.4319853 -0.22007833 0.26171014  0.9196682 0.41131088
[3,]   0.14770195 0.1921369  0.06219956 0.27846391  1.1409913 0.55349628
[4,]   0.03639913 0.8905720 -0.12118612 0.20868257  0.1441628 0.22540192
[5,] -14.76172692 4.4866371  0.47327009 3.50735950 -1.5298982 2.56084962
           [,18]       [,19]       [,20]       [,21]      [,22]       [,23]
[1,] -0.01006053  0.08058213  0.04826516  0.08065891 -0.0162683 -0.14131251
[2,]  0.08807713  0.97734272 -0.54663873 -0.97985679 -0.4098898  0.24164917
[3,]  0.36865175  0.60083092 -0.77282524  1.27618545 -0.6794463 -0.18584679
[4,] -0.10594479  0.10478270 -1.21436544 -0.17241829 -0.2895050 -0.07518825
[5,]  1.61954547 -0.81071145  9.88293037 -0.76202989  2.5481309  2.83861093
           [,24]       [,25]       [,26]       [,27]       [,28]       [,29]
[1,] -0.15414001 -0.05924989 -0.02234328  0.05366529  0.02330964  0.01221998
[2,] -0.26170549  0.21903772  0.01189194  0.17607641 -0.02150335  0.03520131
[3,] -0.27274221  0.17238788 -0.14485380  0.52125554 -0.33983255  0.19381123
[4,]  0.02457872  0.50605188  0.10360397  0.02237312 -0.05411052  0.35329195
[5,]  0.01616854 -1.88601065  0.67539368 -2.49402425  0.23559612 -0.02245021
             [,30]       [,31]       [,32]         [,33]        [,34]
[1,] -0.0004730148 -0.26539764 -0.08317794   0.255725446 -0.058979386
[2,]  0.2256763268  0.37463744  0.19408261  -1.204356655 -0.073207107
[3,]  0.4911442403  0.23435054  0.09879181   0.002251206  0.085974585
[4,] -0.3814220432 -0.08001124  0.17177832  -0.216500464  0.004286006
[5,]  0.9189050771  5.21686311  1.16566527 -13.989112741 -3.092112071
           [,35]       [,36]        [,37]       [,38]       [,39]        [,40]
[1,]  0.02679171 -0.04735554  0.047528989  0.03388928  0.06012161   0.07380386
[2,] -0.30356459  0.48428172 -0.288251610 -0.17238088  0.61863618   1.01851730
[3,] -0.05542970 -0.06576641 -0.008205903 -0.18036256  0.17531779   0.80048092
[4,] -0.08408217 -0.18820315  0.784565453 -0.35676218  0.20427453  -0.24302747
[5,] -1.71264237  5.28990585 -5.368725005 -1.30443643 -1.91839278 -10.36830759
           [,41]        [,42]       [,43]      [,44]       [,45]        [,46]
[1,]  0.05486592 -0.004812446  0.02255845  0.1257899  0.02590248  -0.05781669
[2,] -0.16888020  1.038690762 -0.14707298 -0.1704850  0.09585739  -0.04681770
[3,] -0.04950102  0.579149302  0.27543141 -0.5388249 -0.03245686  -0.33855662
[4,] -0.02419832  0.095800189 -0.05508256 -0.2047222  0.06645682  -0.05052562
[5,] -5.30024751  7.625513073 -3.98635533 -0.3319122 -1.33337337 -15.47592044
           [,47]       [,48]       [,49]        [,50]        [,51]       [,52]
[1,]  0.16823945  0.05076861  0.03480997  0.008035774  0.005329545  0.02799855
[2,]  0.12547798  0.21467980  0.05511653 -0.420334851 -0.051188257  0.25887719
[3,]  0.11870738 -0.50120535 -0.02357010  0.387814964  0.125472161 -0.32203824
[4,]  0.09252739  0.30568229 -0.03501199  0.600839087 -0.024838210  0.30432651
[5,] -2.70744757 -7.27907964 -0.81537356  1.396613784 -1.032064699  6.03268902
           [,53]      [,54]      [,55]       [,56]        [,57]        [,58]
[1,]  0.01649458  0.1262815 -0.1585307 -0.04726732 -0.066360533  0.026753366
[2,] -0.12230329  1.1310019 -0.6686103 -0.53890628  0.031827191 -0.046671214
[3,] -0.04688764 -0.2449663 -0.3153409 -0.66869923  0.006044049 -0.015525322
[4,] -0.05392878 -0.3854056 -0.9750561 -0.53891770  0.023069906  0.006144232
[5,] -0.94780197 -5.6094866  2.9031573  2.00279683 -1.237507227 -2.568585103
           [,59]        [,60]       [,61]      [,62]       [,63]       [,64]
[1,] -0.08089451   0.01003462 -0.05021103 -0.3524449 -0.10280067  0.08269093
[2,]  0.15525546  -0.35428176 -0.09872282  0.1373106 -0.02866929 -0.07166053
[3,] -0.20146837  -0.11694346 -0.14053547  0.3113057  0.19743770 -0.40084555
[4,] -0.08908985  -0.12361437  0.24248088 -0.1100309  0.20696844 -0.20537025
[5,] -2.83827646 -10.72506073  3.17868703 26.8521888  2.62343450 -4.15775394
           [,65]        [,66]         [,67]       [,68]      [,69]       [,70]
[1,] -0.04138939 -0.040064574 -0.0007362193  0.05161787 0.09185479 -0.07073663
[2,]  0.03165022 -0.023723173 -0.0414842779 -0.05575432 0.04164251  0.91740433
[3,]  0.07898768 -0.039863476  0.0033408349  0.09038758 0.01133390 -0.88218300
[4,] -0.20888030  0.006748412 -0.0584372685  0.13358913 0.18186504 -0.35438516
[5,] -0.09798309 -3.505200456  6.3041616440  1.39282541 0.46060327 -6.37977198
           [,71]       [,72]       [,73]        [,74]       [,75]        [,76]
[1,]  0.00997169 -0.44474303 -0.06120406  0.004393256 -0.05562603 -0.009599279
[2,]  0.07090067  0.12876794  0.01357226 -0.043425860  0.05506003 -0.151828300
[3,]  0.12864136  0.02028597 -0.14933141 -0.085244679 -0.02742979  0.179285839
[4,]  0.13565822 -0.07601688 -0.12735312 -0.075753270  0.08259904  0.363735817
[5,] -4.55281363 -8.60120001  0.44876409  7.066741054 -3.19862697 -8.143945254
           [,77]       [,78]       [,79]        [,80]       [,81]      [,82]
[1,] -0.07478471 -0.31110067  0.02679439  -0.17408152 -0.11759396  0.1758414
[2,] -0.14236384 -0.12873642 -0.03188960   0.14231290  0.14274313  0.1609918
[3,]  0.01343117 -0.04619204 -0.17126568   0.04422442  0.49233181 -0.4833354
[4,] -0.11156192 -0.10543498  0.02846461   0.09502682 -0.04951795 -0.3072073
[5,] -0.79301670  4.90987334  0.48688155 -19.05940948  2.25466545 -8.2848005
           [,83]       [,84]        [,85]       [,86]       [,87]      [,88]
[1,] -0.05378226  0.01620354  -0.05835848 -0.06733417 -0.04310416  0.1353579
[2,] -0.16883827 -0.10898358  -0.20663313 -0.09900863 -0.13248355  0.8489578
[3,] -0.12120503 -0.04386876   0.36633905  0.20218117 -0.13989494  0.3186798
[4,]  0.18758772 -0.08590368   0.44227925 -0.06613681  0.18255571  0.3542420
[5,]  4.59627534  6.03977904 -10.51526629  0.42443243  2.72109764 -0.3581265
            [,89]         [,90]        [,91]     [,92]       [,93]       [,94]
[1,]  -0.12388657 -3.001223e-01   0.03336805 0.0105249 -0.04009129  0.11849503
[2,]  -0.01879418 -6.328088e-03  -0.05839125 0.5710398 -0.40738926 -0.08219293
[3,]   0.12457682 -2.890082e-02   0.60045376 0.3187342 -0.11998687  0.01297986
[4,]   0.09071514 -2.949175e-04   0.09038809 0.2036538  0.14067389  0.26486812
[5,] -16.39914252 -1.165707e+01 -12.94456049 0.4784692  2.21596102  1.63323584
           [,95]       [,96]       [,97]       [,98]       [,99]      [,100]
[1,]  0.03707146 -0.06795651  0.01399092  0.02933869 -0.05933928 -0.02022425
[2,]  0.03013566  0.35827961  0.12411900  0.66830914 -0.08208146  0.05506468
[3,]  0.04984559  0.37199214  0.59768066  0.94038550 -0.01101590  0.11825172
[4,] -0.13445471  0.18693061  0.80310676  0.04897772  0.09738021  0.06315884
[5,] -1.02482065  8.73490708 -4.92379620 -0.38027307  2.05294513  0.64650161
         [,101]     [,102]      [,103]      [,104]     [,105]     [,106]
[1,]  0.2386085 0.05205552 -0.08927244 -0.09035823 -0.6063005  0.2298775
[2,]  0.3512321 0.94203704  1.35603668 -0.47120933  0.7948743 -1.0406764
[3,] -0.5142977 0.75232868  0.97568584  0.26150542  7.1915943  0.5990978
[4,] -3.1800315 0.49314140  0.17383601  1.05909645 -2.5465934 -0.3629324
[5,]  0.1366017 2.17928800  4.40436091  4.83157904  2.4416556 -3.0631209
          [,107]      [,108]     [,109]      [,110]      [,111]    [,112]
[1,] -0.01894389  0.04674641  0.4084721  0.06068036  0.01546495 0.1322148
[2,]  0.48719139 -0.27997529  0.8526412  0.25212819 -0.12841575 0.7995035
[3,]  0.23205027 -0.40707330 -1.3105204 -0.42616656  0.47401271 0.8835799
[4,] -0.54871361 -0.18851428  1.9789427 -0.31708970  0.22001569 0.3986271
[5,] -0.37742837 -1.33643490  5.2969272 -3.10163529 -1.55295033 1.2928041
          [,113]      [,114]       [,115]     [,116]      [,117]     [,118]
[1,] 0.007662545  0.09988463  0.145840484  0.1024545 -0.11769112 -0.2001175
[2,] 0.170247539 -0.19363359  0.009969699  0.1144225 -0.06334574 -0.2342516
[3,] 0.014844646 -0.23028030  0.006850185  0.2824710 -0.16104318 -0.4194008
[4,] 0.031485341 -0.10473602 -0.011398193 -0.3097834 -0.50777673 -0.3470900
[5,] 1.599348819 -0.18846228 -5.712934722 -0.8189174  1.87101648  2.9212712
         [,119]     [,120]      [,121]       [,122]      [,123]      [,124]
[1,] 0.09471807  0.1130413 -0.05158016 -0.009255558  0.05268428 -0.08885085
[2,] 0.04457850  0.1374791 -0.27943896  0.024513231 -0.37646922  0.52281397
[3,] 0.17052701  0.4886746 -0.01194357  0.306694132  0.26451682 -0.34081443
[4,] 0.16504237  0.4869065  0.08805378  0.173056241  0.04452961 -0.18800133
[5,] 0.93891937 12.0179440  1.19955003 -0.255919197 -1.29144575 -4.97489791
          [,125]      [,126]     [,127]      [,128]      [,129]       [,130]
[1,]  0.03804128  0.21738263  0.1071019  0.02677213 -0.01188676  0.066718293
[2,]  0.23958559 -0.22956649 -0.1293724 -0.24703287  0.08459353 -0.002949392
[3,] -0.04262501  0.33190291 -0.3037319  0.60787171  0.06680361 -0.149810314
[4,]  0.27506057 -0.06004528 -0.1322323  0.67148435 -0.13527336 -0.096535510
[5,]  0.78227919 -0.94998283 -1.9285334  0.12200297 10.11875057 -0.968574513
          [,131]       [,132]     [,133]     [,134]      [,135]      [,136]
[1,] -0.05970780 -0.008679655  0.1547308  0.1383191  0.07527548  0.05962843
[2,] -0.09551346 -0.420906283  0.2106165 -1.0239744  0.16017900  0.66933660
[3,] -0.49052630 -0.150718682 -0.1388989 -0.2749802 -0.16530208  0.22464733
[4,] -0.41004632  0.291334314  0.7380737 -0.2903714 -0.21571934 -0.30891216
[5,]  0.71453901  0.569753533 -0.7002368 -2.6078108 -4.82277191 -1.37762919
          [,137]      [,138]       [,139]      [,140]      [,141]      [,142]
[1,] -0.01165637  0.06965512 -0.042529040  0.08605934 -0.01552663  0.03694875
[2,] -0.57470776 -0.01403147 -0.013985063  0.08620651 -0.37494971  0.14811760
[3,] -0.42129025 -0.07782227 -0.042994896 -0.14772821 -0.31825185  0.02242545
[4,]  0.03739733  0.00151354 -0.005750002  0.48732787 -0.06674310 -0.07326151
[5,] -0.34237267 -3.92207868 -7.110402730 -3.75340719 -3.56643887 -1.96886343
          [,143]       [,144]      [,145]      [,146]     [,147]      [,148]
[1,] -0.04031168  0.025608024 -0.01879067 -0.03306192  0.2537349  0.01857710
[2,] -0.06785872 -0.062509632 -0.01184425 -0.58335893 -0.2399527  0.01411565
[3,]  0.20069865 -0.127112728 -0.03356285  0.06986585  0.0452182 -0.07679947
[4,]  0.03168676 -0.006763502 -0.03305051 -0.73993021 -0.1785329  0.07117203
[5,]  0.57366402 -2.786992939 -3.72751580  3.76911264 -6.5063331 -1.88105845
           [,149]       [,150]      [,151]      [,152]      [,153]      [,154]
[1,]  0.103288784  0.022415911  0.03168142  0.07722191  0.07125857  0.08089592
[2,]  0.144653288 -0.003688951  0.20771394  0.03263149  0.06165718  0.02033496
[3,]  0.106305882 -0.011417365  0.01883047  0.07323220 -0.31521121 -0.01422248
[4,] -0.008329157  0.026040163  0.05001719  0.02261850 -0.08361253 -0.03256338
[5,]  2.585865541 -5.170108284 -1.60617720 -6.39940681 -7.50624108 -2.01246105
          [,155]     [,156]       [,157]      [,158]      [,159]      [,160]
[1,]  0.09829196  0.1120383  0.091290307  0.11584785  0.15764644  0.02517606
[2,]  0.15838546 -0.4125595 -0.026018234  0.03424830  0.18379288  0.02039404
[3,]  0.14990208 -1.6818112  0.003842039 -0.01609933  0.05421102 -0.24829109
[4,] -0.02853252 -0.3736089  0.039053891  0.02482566  0.09809183 -0.14755343
[5,]  1.43300021 -8.2081280 -8.955649844 -6.22857706 -9.40233016 -4.00437874
           [,161]       [,162]       [,163]      [,164]       [,165]
[1,]  0.030454374  0.005229061   0.14745007  0.06826318 -0.005805539
[2,] -0.034187071 -0.170814405  -0.09127425 -0.03413098  0.034419469
[3,]  0.005258870  0.226947409  -0.03092286  0.01923485 -0.021717756
[4,]  0.001160607 -0.198636368   0.60271704 -0.05754995  0.007596287
[5,]  0.163229289 -0.299694850 -11.89731729 -4.64257574  0.779832228
          [,166]      [,167]       [,168]        [,169]       [,170]
[1,]  0.04305948  0.05340929   0.23011376 -0.0006804426  0.024599592
[2,] -0.02656849 -0.27494235  -0.04759019  0.0119855193  0.041713900
[3,]  0.02873286 -0.37771992  -0.04486729  0.0111473342  0.041408673
[4,]  0.01167391  0.55365161   0.22687168  0.1430393930  0.001833882
[5,] -3.18951782 -1.15056954 -10.93917339  1.7823512228 -0.268490212
          [,171]      [,172]       [,173]       [,174]      [,175]     [,176]
[1,] -0.01433290  0.02722652  0.026018594  -0.09934272 -0.04866599 -0.0804885
[2,] -0.00161266 -0.01287118 -0.010345933   0.07956391 -0.07163641  0.2563591
[3,] -0.11609602  0.10949330  0.008685134   0.04021899 -0.67532141  0.1425894
[4,]  0.01295005 -0.05548110  0.027056853  -0.42749782 -0.70123030  0.1011791
[5,]  1.51800661 -1.87346831 -1.932688275 -15.32003517  2.37656032 -5.8987341
          [,177]    [,178]     [,179]      [,180]      [,181]       [,182]
[1,] -0.03122580 0.3923741 -0.1522263  0.01387516 -0.04405376 -0.020319441
[2,]  0.01361661 0.9821501 -0.3937627 -0.21976745 -0.06822359  0.084539462
[3,]  0.03919566 1.4052051 -0.5267384 -0.05348111 -0.37173787  0.006197581
[4,]  0.03087236 0.9716418 -0.5687771  0.40719210 -0.18853887 -0.363755051
[5,]  3.04435227 3.9230023  8.5395895 -0.74270325 -2.81876794 -0.003129918
          [,183]     [,184]    [,185]      [,186]      [,187]      [,188]
[1,]  0.04619226 -0.1865192 -0.461636  0.13308434 -0.03406584 -0.02260939
[2,] -0.51678067  0.5551459 -1.788954  0.49227251  0.09035485 -0.29597839
[3,]  1.07994558  0.3002365 -0.917324  0.09556089  0.13648932  0.36976100
[4,]  0.09483695  1.4762739  1.345954  0.43609562 -0.07120954  0.59247577
[5,] -2.86686034  4.1966943  2.142175 -1.76829156 -2.05535476  0.54339201
          [,189]      [,190]      [,191]      [,192]      [,193]       [,194]
[1,]  0.03122854 -0.05005939 -0.21843531 -0.09410384  0.04851126  0.003455175
[2,]  0.09364822  0.24701336 -0.16958618 -0.28769290 -0.50790839  0.195415145
[3,] -0.12282667  0.15036440 -0.34322516 -0.11919454 -0.29420326 -0.327377399
[4,] -0.05232798  0.55722339 -0.01191703 -0.15314465  0.10907216  0.092834363
[5,]  0.72379235  0.77114667 -2.18304379  6.69544516 -4.12215391 -1.025667995
          [,195]       [,196]     [,197]      [,198]      [,199]      [,200]
[1,] -0.06553308  0.001052558 -0.1783757 -0.09914535 -0.11344582  0.09479269
[2,] -0.04942156  0.192670217  0.6668382  0.02465177 -0.09297970  0.07515087
[3,]  0.01866546 -0.021396719 -0.9919839 -0.06624163  0.02476508  0.24304835
[4,]  0.01921058  0.083577423 -0.1132233 -0.03609172  0.06380090  0.18032682
[5,]  0.78069034  4.181157589 -4.9790268 -5.52603167 -1.49663837 -2.55352198
          [,201]      [,202]      [,203]      [,204]       [,205]      [,206]
[1,] -0.23383860 -0.08993935 -0.05993417 -0.01132878  0.009603434 -0.04660046
[2,] -0.05352490 -0.37256976 -0.08395470 -0.06733110 -0.905494988  0.44256870
[3,] -0.05744818  0.29008171 -0.03240443  0.02463175 -0.508675265 -0.22027262
[4,]  0.03895087 -0.31798822 -0.04079500  0.09364354 -0.281167498 -0.01026686
[5,] -4.05114094 -1.75970256  1.51860242 -1.52069610 -2.749919607 -3.10761530
           [,207]        [,208]       [,209]       [,210]      [,211]
[1,]  0.008667404 -0.0135401407  0.014991068  0.136467214 -0.03475386
[2,]  0.009593630 -0.1420236387 -0.028553409 -0.077035437 -0.03368546
[3,]  0.020383986  0.0862902856  0.001600125 -0.001161309 -0.01639970
[4,]  0.005385690  0.0007758484 -0.110692667  0.063182033 -0.03830633
[5,] -3.885254934 -8.4181194341  5.158298683 -7.585506969  3.47245716
          [,212]       [,213]      [,214]       [,215]       [,216]      [,217]
[1,]  0.02379568  0.045754575  0.24346661 -0.015137347  0.005054379 -0.03501973
[2,]  0.02533997 -0.049119222 -0.01021564 -0.002576104  0.479876185  0.18987603
[3,]  0.10755924 -0.015368213  0.01410280  0.136949227  0.409634977  0.07311577
[4,]  0.15515433 -0.009572094  0.01893432  0.106960420  0.152328003 -0.60689483
[5,] -2.53872862  2.273429923 -6.75561203  0.214976970 -0.538449480 -0.65777756
           [,218]     [,219]      [,220]       [,221]        [,222]      [,223]
[1,]  0.065648204 -0.1918557 -0.21659644  0.003055776 -3.324569e-02  0.02461426
[2,]  0.030439292  0.1864587 -0.05243454  0.005622446  2.245024e-02 -0.32168639
[3,] -0.223453365 -0.2115671 -0.02222156 -0.016492777  6.862687e-02 -0.05545155
[4,] -0.008801991  0.4984232 -0.00180395  0.009798886 -7.259226e-05 -0.17057227
[5,]  5.200989279 11.4083582 -2.92561814 -0.441753706 -8.879111e+00  1.30340705
           [,224]      [,225]      [,226]      [,227]      [,228]      [,229]
[1,] -0.023447425  0.06909338  0.01221833 -0.01617890  0.02310460  0.08393972
[2,] -0.049410108 -0.05104247 -0.07474333  0.18036787 -0.55319830  0.03658127
[3,] -0.001090005  0.04073767  0.04136036 -0.08507372 -0.03256302 -0.02898953
[4,] -0.001678141  0.04702490  0.48409685 -0.30419452 -0.07260835 -0.01925247
[5,] -0.808465184 -8.12743593  7.31249862  0.96245455  1.02702105  1.59113124
          [,230]      [,231]      [,232]      [,233]     [,234]      [,235]
[1,] -0.01043464 -0.03076702  0.01173849 -0.01687044  0.1244041  0.03147853
[2,] -0.05502378  0.05218179 -0.01851073  0.21213952  0.2935620  0.07140946
[3,] -0.08483239  0.14123632 -0.01835034  0.48316900  0.1433885  0.10180752
[4,]  0.08239944  0.06178442  0.04861970  0.08404696  0.1214794 -0.20546807
[5,] -1.31936476 -2.72085419 -7.21414723 -8.89966924 -1.6397756 -0.81081307
          [,236]
[1,]  0.14678850
[2,] -0.19798863
[3,] -0.09435322
[4,] -0.20361795
[5,]  6.60407797
colnames(pdmif$Coefficients) <- colnames(data4y)

length(pdmif$Coefficients[1,which(groupmem$type == 3)])
[1] 41
length(pdmif$Coefficients[1,which(groupmem$type == 2)])
[1] 148
length(pdmif$Coefficients[1,which(groupmem$type == 1)])
[1] 47
pdmif$Coefficients[which(groupmem$type == 3)]
 [1]  -0.093408741   0.254449488   0.036399129 -14.761726920   0.431985299
 [6]   0.890572009   4.486637086  -0.026501919  -0.220078329   0.473270089
[11]   0.095784655   0.261710143   0.278463911   3.507359505   0.167181286
[16]   0.919668180   1.140991268  -1.529898214   0.411310883   0.225401921
[21]   2.560849620   0.368651748   1.619545472   0.048265165   0.080658910
[26]  -0.022343283  -0.047355537  -1.918392782   0.073803858   0.800480917
[31]  -0.243027467  -0.024198318  -0.004812446  -0.147072977   0.275431405
[36]  -0.055082556  -0.170484972  -0.331912185 -15.475920442   0.168239445
[41]   0.125477978
# pdmifshort <- pdmif$Coefficients[,colnames(pdmif$Coefficients)!="30003"]
# groupmemshort <- as.data.frame(groupmem[rownames(groupmem) != "30003",])
# names(groupmemshort) <- "type"

beeswarm(c(pdmif$Coefficients[1,],pdmif$Coefficients[2,],pdmif$Coefficients[3,]) ~ c(rep(1,236),rep(2,236), rep(3,236)),
          #col = c(rgb(0.25, 0.63, 1, 0.75), rgb(1, 0.88, 0.6, 0.75), rgb(0.97, 0.43, 0.37, 0.75)),
          pwcol = c(rep(groupmem$type), rep(groupmem$type),rep(groupmem$type)),
          pch=19, method="swarm", cex=0.5, corral="wrap", ylab="Value", xlab=NA, labels=c("mines_diff","lag_diff","lag_diff2"))

legend("topright", legend = c("1", "2", "3"),
       col = 1:3, pch = 19)

# allcomp %>% filter(fips == "30003") %>% ggplot(aes(y = uer, x = year))+
#   geom_line()+
#   geom_line(aes(y = mines_diff))


allcomp_cc_typespdmif <- subset(allcomp_cc_types, fips %in% allcomp_pdmif$fips)

urban_pdmif <- allcomp_cc_typespdmif %>% subset(type == 1)
medium_pdmif <- allcomp_cc_typespdmif %>% subset(type == 2)
rural_pdmif <- allcomp_cc_typespdmif %>% subset(type == 3)

cc_ruralpdmif = feols(FE_diffuer, rural_pdmif, se = "twoway")
cc_mediumpdmif = feols(FE_diffuer, medium_pdmif, se = "twoway")
cc_urbanpdmif = feols(FE_diffuer, urban_pdmif, se = "twoway")

etable(cc_rural, cc_ruralpdmif, cc_medium, cc_mediumpdmif, cc_urban, cc_urbanpdmif)

2.0.4 Binary change in mines

Likely to exclude this estimation as results are not significant.

# ----- on binary indicator for whether the change in mines is negative from the previous year
allcomp$lag_closure2[which(is.na(allcomp$lag_closure2))] <- 0
allcomp_cc$lag_closure2[which(is.na(allcomp_cc$lag_closure2))] <- 0
etable("Model 10" = feols(diff_uer ~ mine_closure + lag_closure + lag_closure2 + diff_log_realgdp_pc | fips + year, allcomp, se = 'twoway'), "Model 11" = feols(diff_uer ~ mine_closure + lag_closure + lag_closure2 + mine_closure:ruc_bin +
               lag_closure:ruc_bin + lag_closure2:ruc_bin + diff_log_realgdp_pc | fips + year, allcomp, se = 'twoway'), "Model 10 CC" = feols(diff_uer ~ mine_closure + lag_closure + lag_closure2 + diff_log_realgdp_pc | fips + year, allcomp_cc, se = 'twoway'),"Model 11 CC" = feols(diff_uer ~ mine_closure + lag_closure + lag_closure2 + mine_closure:ruc_bin +
               lag_closure:ruc_bin + lag_closure2:ruc_bin + diff_log_realgdp_pc | fips + year, allcomp_cc, se = 'twoway'))

2.0.5 Change in coal production

Likely to exclude this estimation as results are not significant.

3 Overall Economy Model

Coefficient interpretation….mines_diff impact on CHANGE in UER and CHANGE in LOG of emp, unemp, lf, and pop. Does this mean that the coefficient on mines_diff translates to “a one-unit increase in mines is correlated with a x% increase in the CHANGE in log of emp, unemp, lf and pop.”

Isn’t the question we want to answer the impact of mines_diff on the log of emp, unemp, lf, pop? Or are we comparing the change in the CHANGE in log of emp, etc compared to mean change?

model_uer <- feols(FE_diffuer, allcomp, se = 'twoway')
model_emp <- feols(diff_log_employed ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp + diff_log_pop | fips + year, allcomp, se = 'twoway')
model_unemp <- feols(diff_log_unemployed ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp + diff_log_pop | fips + year, allcomp, se = 'twoway')
model_lf <- feols(diff_log_lf ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp + diff_log_pop | 
               fips + year, allcomp, se = 'twoway')
model_pop <- feols(diff_log_pop ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp | 
               fips + year, allcomp, se = 'twoway')

model_uercc <- feols(FE_diffuer, allcomp_cc, se = 'twoway')
model_empcc <- feols(diff_log_employed ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp + diff_log_pop | fips + year, allcomp_cc, se = 'twoway')
model_unempcc <- feols(diff_log_unemployed ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp + diff_log_pop | fips + year, allcomp_cc, se = 'twoway')
model_lfcc <- feols(diff_log_lf ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp + diff_log_pop | 
               fips + year, allcomp_cc, se = 'twoway')
model_popcc <- feols(diff_log_pop ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp | 
               fips + year, allcomp_cc, se = 'twoway')


etable("Model 1"= model_uer,
       "Model 2" = model_emp,
       "Model 3" = model_unemp,
       "Model 4" = model_lf,
       "Model 5" = model_pop)
# Performs same regressions on subset of coal counties only (defined as counties 
# that have had active mines between 2002-2019).
etable("Model 1 CC"= model_uercc,
       "Model 2 CC" = model_empcc,
       "Model 3 CC" = model_unempcc,
       "Model 4 CC" = model_lfcc,
       "Model 5 CC" = model_popcc)
model_list = list(model_uer, model_emp, model_unemp, model_lf, model_pop)
var_rows <-  c("Dependent Var.:", "Change Active Mines","Change Active Mines (t-1)","Change Active Mines (t-2)")

# econ_df <- data.frame(matrix(nrow = 4))
# for(k in 1:length(model_list)){
#   model = etable(model_list[k])
#   save_short <- subset(model, rownames(model) %in% var_rows)
#   econ_df <- cbind(econ_df, save_short)
# }
# econ_df
# test <- as.matrix(pvalue(model_emp)[1:3])

econ_df <- as.data.frame(rbind(create_cols(model_uer), create_cols(model_emp), create_cols(model_unemp), create_cols(model_lf), create_cols(model_pop)))
colnames(econ_df) <- c("t", "t-1", "t-2")
econ_df$model <- c("UER", "EMP", "UNEMP", "LF", "POP")
econ_dflong <- pivot_longer(econ_df, !model, names_to = "time", values_to = "fill")
#econ_dflong$fill <- as.factor(econ_dflong$fill)
econ_dflong$fill[which(econ_dflong$fill == 0)] <- NA
# writexl::write_xlsx(econ_dflong, here("data/"fulleconomydf.xlsx"))
ggplot(econ_dflong, aes(x = time, y = model, fill = fill))+
  scale_y_discrete(limits = rev(c("UER", "EMP", "UNEMP", "LF", "POP")), labels = rev(c("Model 1: \u0394 UER","Model 2: \u0394 (log) Employed persons", "Model 3: \u0394 (log) Unemployed persons", "Model 4: \u0394 (log) Labour force", "Model 5: \u0394 (log) Population")))+
  scale_x_discrete(limits = c("t", "t-1", "t-2"), labels = c("0","+1", "+2"))+
    geom_tile(color = "white",
            lwd = 1.5,
            linetype = 1)+
  scale_fill_gradientn(colours=c("navy","royalblue","lightskyblue","white", "mistyrose","red", "darkred"),limits = c(-4,4), na.value = "grey98", labels = c("(-)***","(-)**", "(+/-)*", "(+)**","(+)***"),labs(colour="Sign and Significance Level", size = 0.1))+
  ylab("Outcome Variable")+ 
  xlab("# years following mine closure")+
  theme(panel.background = element_blank(), panel.border = element_blank(), legend.title=element_text(size=8), )

3.1 Effect of REE investments:

Research Question: Do proximate renewable energy investments impact county-level employment impacts of mine closures?

Employs standard two-way fixed effects panel regressions with county-clustered standard errors to a panel dataset of 3,072 contiguous counties observed between 2002-2019. Interacts binary indicator for whether the total number of active mines in a county decreased from the previous year and whether REE investments exceeded 0.1% of county GDP.

Results: 1. No statistically significant impact of renewable energy investments detected.

summary(allcomp$REE_inv_scaled_realgdp[allcomp$REE_inv_scaled_realgdp != 0])
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
  0.0000   0.0049   0.0221   0.6541   0.0982 503.9275 
#7728 observations of non-zero REE. 13.9% of observations have non-zero REE
# 460 observations have investment >= 1*county real GDP (8 also with active mines)
# 75 observations have investment >= 10*county real GDP
# 6 observations have investment >= 100*county real GDP
# 1st quartile upper limit is 0.0049* county real GDP

### 465 observations in coal subset with 162 with mines_diff != 0
test <- subset(allcomp, REE_inv_scaled_realgdp != 0)
quantile(test$REE_inv_scaled_realgdp, probs = seq(.1, .9))
       10% 
0.00115976 
summary(test$REE_inv_scaled_realgdp)
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
  0.0000   0.0049   0.0221   0.6541   0.0982 503.9275 
hist(test$REE_inv_scaled_realgdp[test$REE_inv_scaled_realgdp < 1], breaks = 100)

##### Preferable to use indicator for whether ANY REE as only 162 observations?
##### (of 7728 with REE investments) have non-zero mines_diff
## Went with top 10th percentile of data (7020 observations 152 with non-zero mines_diff)

etable("REE Model" = feols(diff_uer ~ mines_diff + lag_diff + lag_diff2 + mines_diff:REE_bin_top90 +
                             lag_diff:l(REE_bin_top90,1) + lag_diff2:l(REE_bin_top90,2) + REE_bin_top90 + diff_log_realgdp_pc | fips + year, allcomp, panel.id = ~fips+year,  se = 'twoway'),
       "REE Model CC" = feols(diff_uer ~ mines_diff + lag_diff + lag_diff2 + mines_diff:REE_bin_top90 +
                             lag_diff:REE_bin_top90 + lag_diff2:REE_bin_top90 + REE_bin_top90 + diff_log_realgdp_pc | fips + year, allcomp_cc,  se = 'twoway'), signifCode = c(`***` = 0.001, `**` = 0.01, `*` = 0.05, . = 0.1))
ree_model_main = feols(diff_uer ~ mines_diff + lag_diff + lag_diff2 + mines_diff:REE_bin_top90 +
                             lag_diff:REE_bin_top90 + lag_diff2:REE_bin_top90 + REE_bin_top90 + diff_log_realgdp_pc | fips + year, allcomp,  se = 'twoway')

etable(feols(diff_uer ~ mines_diff + mines_diff:REE_bin_top90 + mines_diff:l(REE_bin_top90, 1) + mines_diff:l(REE_bin_top90, 2) + diff_log_realgdp_pc | fips + year, allcomp, panel.id = ~fips+year,se = 'twoway'),
       feols(diff_uer ~ mines_diff + mines_diff:REE_bin_top90 + diff_log_realgdp_pc | fips + year, allcomp, panel.id = ~fips+year,se = 'twoway'),
       feols(diff_uer ~ mines_diff + mines_diff:l(REE_bin_top90, 1) + diff_log_realgdp_pc | fips + year, allcomp, panel.id = ~fips+year,se = 'twoway'),
       feols(diff_uer ~ mines_diff + mines_diff:l(REE_bin_top90, 2) + diff_log_realgdp_pc | fips + year, allcomp, panel.id = ~fips+year,se = 'twoway'))
etable(feols(diff_uer ~ lag_diff2 + diff_log_realgdp_pc | fips + year, allcomp, panel.id = ~fips+year,se = 'twoway'),
       feols(diff_uer ~ lag_diff2 + lag_diff2:l(REE_bin_top90, 1) + diff_log_realgdp_pc | fips + year, allcomp, panel.id = ~fips+year,se = 'twoway'),
       feols(diff_uer ~ lag_diff2 + lag_diff2:l(REE_bin_top90, 2) + diff_log_realgdp_pc | fips + year, allcomp, panel.id = ~fips+year,se = 'twoway'),
       feols(diff_uer ~ lag_diff2 + lag_diff2:l(REE_bin_top90, 2) + lag_diff2:l(REE_bin_top90, 1) + diff_log_realgdp_pc | fips + year, allcomp, panel.id = ~fips+year,se = 'twoway'), signifCode = c(`***` = 0.001, `**` = 0.01, `*` = 0.05, . = 0.1))

4 Spatial models

4.1 Spatial models

4.1.1 Tests for cross-sectional dependence

Cross-sectional dependence detected. Moran’s I test and lagrange multiplier tests remain to be run!

##############################################################
# library(lmtest)
# mainplm <- plm(diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc, allcomp_full, effect="twoways", index = c("fips","year"))

# Confirms fixed effects is the way to go
phtest(diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc, 
        data = allcomp_full, effect = "twoways", index = c("fips", "year"),
        test = c("cd", "sclm", "bcsclm", "lm", "rho", "absrho"),
        w = fmat)

# Confirms presence of cross sectional dependence
tests <- c("cd", "lm", "sclm", "rho", "absrho")

cdtest <- lapply(tests,function(x) pcdtest(diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc, 
        data = allcomp_full, effect = "twoways", index = c("fips", "year"),
        test = x,
        w = fmat))

pcdtest(diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc, 
        data = allcomp_full, effect = "twoways", index = c("fips", "year"),
        test = "rho",
        w = fmat)

# Randomization-based test of spatial dependence for panel models
# All of the following confirm cross-sectional dependence robust to global 
# dependence by common factors and persistence (serial correlation) in the data (Millo 2017)
#"rho" for the average correlation coefficient
# These return identical results...why?

rwtest(diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc, 
        data = allcomp_full, test = "rho", w = fmat, effect = "twoways", index = c("fips", "year"))
# "cd" for Pesaran's CD statistic
rwtest(diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc, 
        data = allcomp_full, test = "cd", w = fmat, effect = "twoways", index = c("fips", "year"))

# "sclm" for the scaled version of Breusch and Pagan's LM statistic,
rwtest(diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc, 
        data = allcomp_full, test = "sclm", w = fmat, effect = "twoways", index = c("fips", "year"))


# Below not required
# 
# sphtest(diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc, data = allcomp_full, effect = "twoways", listw = fmatlw, spatial.model = "error", method = "ML")
# sphtest(diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc, data = allcomp_full, effect = "twoways", listw = fmatlw, spatial.model = "lag", method = "ML")
# sphtest(diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc, data = allcomp_full, effect = "twoways", listw = fmatlw,spatial.model = "sarar",method = "ML")


# MORAN'S I TEST
#moran.lm<-lm.morantest(allcomp_full$diff_uer, fmatlw, alternative="two.sided")


# Vetor memory exhausted
# LAGRANGE MULTIPLIER TEST
# Locally robust LM tests for spatial lag (error) correlation sub spatial error (lag) correlation in panel models
#slmtest(diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc,
#        data = allcomp_full, listw = fmatlw, test="lme", model="within")
#slmtest(diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc,
#        data = allcomp_full, listw = fmatlw, test="lml", model="within")
#slmtest(diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc,
#        data = allcomp_full, listw = fmatlw, test="rlme", model="within")
#slmtest(diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc,
#        data = allcomp_full, listw = fmatlw, test="rlml", model="within")
# This does not include 7 LA counties for which no employment indicators 
# were recorded in 2005 and 2006. Therefore, use allcomp_full as created 
# in the spatial matrix initiation section.

# Broomfield County, Colorado was only created in 2001 so no observation 
# exists for 2001 - will replace with 0 for completeness. It is only the 
# first observation...

allcomp_full$diff_log_realgdp_pc[which(allcomp_full$fips == "08014" & allcomp_full$year == 2002)] <- 0
allcomp_full$diff_log_realgdp[which(allcomp_full$fips == "08014" & allcomp_full$year == 2002)] <- 0
allcomp_full$diff_log_pop[which(allcomp_full$fips == "08014" & allcomp_full$year == 2002)] <- 0

fmdiff_uer <- diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc

# Required to extract Log Likelihood of error and SARAR model
#fixInNamespace("spsararlm", "splm")
#fixInNamespace("sperrorlm", "splm")
# (NOT DONE AS SPLAGLM RETURNS A MEASURE FOR LL) 
#fixInNamespace("splaglm", "splm")
# This third function (which returns a value for loglikelihood, transforms it according to:
# ens <- as.numeric((time * ldet  - ((n*time/2) * log(2 * pi)) - (n*time/2) * log(s2) - 1/(2 * s2) * SSE)) 
# and then returns ens instead of LL.......

#And manually add "ll=LL" as an element to the "return" list at the end of
#the function so that it reads:
#return <- list(coeff = betas, lambda = lambda, s2 = s2, rest.se = rest.se, 
#       lambda.se = lambda.se, asyvar1 = asyvar1, ll=LL)

sp_err_mines <- spml(fmdiff_uer, allcomp_full, 
                     index = NULL,  listw = fmatlw, lag = FALSE, na.action = na.fail, 
                     spatial.error = "b",  model = "within", effect = "twoways", quiet = FALSE)

 Spatial Error Fixed Effects Model

Spatial fixed effects model
 Jacobian calculated using neighbourhood matrix eigenvalues
Computing eigenvalues ...

rho: -0.4910988  function: -301880.6  Jacobian: -57.97902  SSE: 53155.03 
rho: 0.07845029  function: -284733.9  Jacobian: -1.615293  SSE: 29657.86 
rho: 0.430451  function: -275948.2  Jacobian: -54.78956  SSE: 20849.69 
rho: 0.6479993  function: -272809.1  Jacobian: -139.9342  SSE: 17608.26 
rho: 0.929466  function: -274515.6  Jacobian: -388.3001  SSE: 15933.04 
rho: 0.7149123  function: -272460.3  Jacobian: -178.9056  SSE: 16951.91 
rho: 0.7310491  function: -272432  Jacobian: -189.5428  SSE: 16817.68 
rho: 0.7440642  function: -272426.3  Jacobian: -198.5226  SSE: 16716.23 
rho: 0.7423415  function: -272426.2  Jacobian: -197.3126  SSE: 16729.31 
rho: 0.7422213  function: -272426.2  Jacobian: -197.2284  SSE: 16730.23 
rho: 0.7422316  function: -272426.2  Jacobian: -197.2356  SSE: 16730.15 
rho: 0.7422315  function: -272426.2  Jacobian: -197.2355  SSE: 16730.15 
rho: 0.7422315  function: -272426.2  Jacobian: -197.2356  SSE: 16730.15 
rho: 0.7422315  function: -272426.2  Jacobian: -197.2356  SSE: 16730.15 
rho: 0.7422315  function: -272426.2  Jacobian: -197.2356  SSE: 16730.15 
sp_lag_mines <- spml(fmdiff_uer, allcomp_full, index = NULL,  listw = fmatlw,
                     lag = TRUE, na.action = na.fail,
                     spatial.error = "none",  model = "within", effect = "twoways", quiet = FALSE)

 Spatial Lag Fixed Effects Model 

Spatial fixed effects model
 Jacobian calculated using neighbourhood matrix eigenvalues
Computing eigenvalues ...

lambda:  -0.4910988     function value:  -302042.5 
lambda:  0.07845029     function value:  -284710.5 
lambda:  0.430451   function value:  -275858.8 
lambda:  0.6479993  function value:  -272730.8 
lambda:  0.9194395  function value:  -274259.1 
lambda:  0.7149204  function value:  -272395 
lambda:  0.7296516  function value:  -272371.7 
lambda:  0.7411351  function value:  -272367.2 
lambda:  0.7396672  function value:  -272367.1 
lambda:  0.7395721  function value:  -272367.1 
lambda:  0.7395797  function value:  -272367.1 
lambda:  0.7395796  function value:  -272367.1 
lambda:  0.7395796  function value:  -272367.1 
lambda:  0.7395796  function value:  -272367.1 
lambda:  0.7395796  function value:  -272367.1 
# Only used to test whether unadjusted loglikelihood yields different AIC and BIC results
# sp_lag_mines_unadjll <- spml(fmdiff_uer, allcomp_full, index = NULL,  listw = fmatlw,
#                      lag = TRUE, na.action = na.fail,
#                      spatial.error = "none",  model = "within", effect = "twoways", quiet = FALSE)

sp_err_lag_mines <- spml(fmdiff_uer, data = allcomp_full, index = NULL,  listw = fmatlw, 
                         lag = TRUE, na.action = na.fail, spatial.error = "b",
                         model = "within", effect = "twoways", quiet = FALSE)

 Spatial SARAR Fixed Effects Model

Spatial fixed effects model
 Jacobian calculated using neighbourhood matrix eigenvalues
Computing eigenvalues ...

neighbourhood matrix eigenvalues
Computing eigenvalues ...

lambda: -1.130119  rho: 0.8  function: -134542.5  Jacobian1: -310.7723  Jacobian2: -241.7962  SSE: 16296.86 
lambda: -1.130119  rho: 0.8  function: -134542.5  Jacobian1: -310.7723  Jacobian2: -241.7962  SSE: 16296.86 
lambda: -1.130119  rho: 0.8  function: -134542.5  Jacobian1: -310.7723  Jacobian2: -241.7962  SSE: 16296.86 
lambda: -0.2784344  rho: 1  function: -132160.8  Jacobian1: -19.03328  Jacobian2: -590.3752  SSE: 14408.59 
lambda: -0.278777  rho: 1  function: -132158.3  Jacobian1: -19.0793  Jacobian2: -590.3752  SSE: 14406.86 
lambda: -0.2784344  rho: 0.9996574  function: -131613.2  Jacobian1: -19.03328  Jacobian2: -559.9158  SSE: 14408.9 
lambda: 0.6973589  rho: 0.7813049  function: -136824  Jacobian1: -167.9175  Jacobian2: -226.4124  SSE: 19619.23 
lambda: 0.1672697  rho: 0.7886324  function: -130005.9  Jacobian1: -7.522001  Jacobian2: -232.3227  SSE: 16953.65 
lambda: 0.1672785  rho: 0.7886324  function: -130005.9  Jacobian1: -7.522813  Jacobian2: -232.3227  SSE: 16953.68 
lambda: 0.1672697  rho: 0.7886326  function: -130005.9  Jacobian1: -7.522001  Jacobian2: -232.3228  SSE: 16953.65 
lambda: -0.07936203  rho: 0.786421  function: -128660.4  Jacobian1: -1.59636  Jacobian2: -230.5233  SSE: 16229.79 
lambda: -0.0788431  rho: 0.786421  function: -128662.1  Jacobian1: -1.575709  Jacobian2: -230.5233  SSE: 16230.98 
lambda: -0.07936203  rho: 0.7864212  function: -128660.4  Jacobian1: -1.59636  Jacobian2: -230.5234  SSE: 16229.79 
lambda: -0.3183288  rho: 0.8474699  function: -128075.2  Jacobian1: -24.75606  Jacobian2: -285.9649  SSE: 15097.2 
lambda: -0.8427513  rho: 1  function: -130111  Jacobian1: -169.4238  Jacobian2: -590.3752  SSE: 12131.11 
lambda: -0.3183263  rho: 0.8474699  function: -128075.2  Jacobian1: -24.75568  Jacobian2: -285.9649  SSE: 15097.2 
lambda: -0.3183288  rho: 0.8474711  function: -128075.2  Jacobian1: -24.75606  Jacobian2: -285.9661  SSE: 15097.19 
lambda: -1.087207  rho: 1  function: -130750  Jacobian1: -286.288  Jacobian2: -590.3752  SSE: 11505.23 
lambda: -0.4515752  rho: 0.8913078  function: -127876  Jacobian1: -49.16736  Jacobian2: -335.3008  SSE: 14286.17 
lambda: -0.4515716  rho: 0.8913078  function: -127876  Jacobian1: -49.16659  Jacobian2: -335.3008  SSE: 14286.18 
lambda: -0.4515752  rho: 0.8913054  function: -127876  Jacobian1: -49.16736  Jacobian2: -335.2978  SSE: 14286.19 
lambda: -0.5823026  rho: 0.840448  function: -128261.9  Jacobian1: -81.09045  Jacobian2: -278.9052  SSE: 14719.61 
lambda: -0.4695  rho: 0.8638277  function: -127887.6  Jacobian1: -53.07414  Jacobian2: -303.2405  SSE: 14556.52 
lambda: -0.459588  rho: 0.8790236  function: -127854.6  Jacobian1: -50.89544  Jacobian2: -320.4507  SSE: 14397.58 
lambda: -0.4595763  rho: 0.8790236  function: -127854.6  Jacobian1: -50.8929  Jacobian2: -320.4507  SSE: 14397.61 
lambda: -0.459588  rho: 0.8790249  function: -127854.6  Jacobian1: -50.89544  Jacobian2: -320.4523  SSE: 14397.57 
lambda: -0.4716558  rho: 0.8873588  function: -127841.9  Jacobian1: -53.55399  Jacobian2: -330.4281  SSE: 14273.05 
lambda: -0.4716525  rho: 0.8873588  function: -127841.9  Jacobian1: -53.55325  Jacobian2: -330.4281  SSE: 14273.06 
lambda: -0.4716558  rho: 0.8873581  function: -127841.9  Jacobian1: -53.55399  Jacobian2: -330.4273  SSE: 14273.06 
lambda: -0.5005359  rho: 0.8822236  function: -127821.3  Jacobian1: -60.19001  Jacobian2: -324.2335  SSE: 14258.34 
lambda: -0.5005312  rho: 0.8822236  function: -127821.3  Jacobian1: -60.18892  Jacobian2: -324.2335  SSE: 14258.35 
lambda: -0.5005359  rho: 0.8822244  function: -127821.3  Jacobian1: -60.19001  Jacobian2: -324.2343  SSE: 14258.33 
lambda: -0.5559804  rho: 0.9013969  function: -127783.6  Jacobian1: -74.01913  Jacobian2: -348.2111  SSE: 13892.72 
lambda: -0.5559777  rho: 0.9013969  function: -127783.6  Jacobian1: -74.0184  Jacobian2: -348.2111  SSE: 13892.73 
lambda: -0.5559804  rho: 0.9013961  function: -127783.6  Jacobian1: -74.01913  Jacobian2: -348.2101  SSE: 13892.73 
lambda: -0.6022142  rho: 0.9063162  function: -127776.7  Jacobian1: -86.65823  Jacobian2: -354.7648  SSE: 13716.79 
lambda: -0.6022113  rho: 0.9063162  function: -127776.7  Jacobian1: -86.6574  Jacobian2: -354.7648  SSE: 13716.79 
lambda: -0.6022142  rho: 0.9063169  function: -127776.7  Jacobian1: -86.65823  Jacobian2: -354.7657  SSE: 13716.78 
lambda: -0.5939949  rho: 0.905218  function: -127776.4  Jacobian1: -84.33699  Jacobian2: -353.2862  SSE: 13750.61 
lambda: -0.5939976  rho: 0.905218  function: -127776.4  Jacobian1: -84.33775  Jacobian2: -353.2862  SSE: 13750.6 
lambda: -0.5939949  rho: 0.9052184  function: -127776.4  Jacobian1: -84.33699  Jacobian2: -353.2868  SSE: 13750.61 
lambda: -0.5939949  rho: 0.9052175  function: -127776.4  Jacobian1: -84.33699  Jacobian2: -353.2855  SSE: 13750.62 
lambda: -0.5942183  rho: 0.9053301  function: -127776.4  Jacobian1: -84.39967  Jacobian2: -353.4367  SSE: 13748.7 
lambda: -0.5941876  rho: 0.9053301  function: -127776.4  Jacobian1: -84.39105  Jacobian2: -353.4367  SSE: 13748.78 
lambda: -0.5942491  rho: 0.9053301  function: -127776.4  Jacobian1: -84.40829  Jacobian2: -353.4367  SSE: 13748.62 
lambda: -0.5942183  rho: 0.9053317  function: -127776.4  Jacobian1: -84.39967  Jacobian2: -353.4389  SSE: 13748.68 
lambda: -0.5942183  rho: 0.9053285  function: -127776.4  Jacobian1: -84.39967  Jacobian2: -353.4346  SSE: 13748.72 
lambda: -0.5942139  rho: 0.9053127  function: -127776.4  Jacobian1: -84.39842  Jacobian2: -353.4134  SSE: 13748.92 
lambda: -0.594183  rho: 0.9053127  function: -127776.4  Jacobian1: -84.38975  Jacobian2: -353.4134  SSE: 13749 
lambda: -0.5942448  rho: 0.9053127  function: -127776.4  Jacobian1: -84.40709  Jacobian2: -353.4134  SSE: 13748.84 
lambda: -0.5942139  rho: 0.9053179  function: -127776.4  Jacobian1: -84.39842  Jacobian2: -353.4204  SSE: 13748.86 
lambda: -0.5942139  rho: 0.9053075  function: -127776.4  Jacobian1: -84.39842  Jacobian2: -353.4064  SSE: 13748.98 
lambda: -0.5942131  rho: 0.9053127  function: -127776.4  Jacobian1: -84.39821  Jacobian2: -353.4133  SSE: 13748.92 
lambda: -0.5941535  rho: 0.9053127  function: -127776.4  Jacobian1: -84.38148  Jacobian2: -353.4133  SSE: 13749.07 
lambda: -0.5942728  rho: 0.9053127  function: -127776.4  Jacobian1: -84.41494  Jacobian2: -353.4133  SSE: 13748.77 
lambda: -0.5942131  rho: 0.9053267  function: -127776.4  Jacobian1: -84.39821  Jacobian2: -353.4322  SSE: 13748.76 
lambda: -0.5942131  rho: 0.9052986  function: -127776.4  Jacobian1: -84.39821  Jacobian2: -353.3945  SSE: 13749.09 
lambda: -0.5942131  rho: 0.9053127  function: -127776.4  Jacobian1: -84.39821  Jacobian2: -353.4133  SSE: 13748.92 
lambda: 0  rho: 0  function: -143439.3  Jacobian1: 0  Jacobian2: 0  SSE: 32217.36 
lambda: 1.490116e-08  rho: 0  function: -143439.3  Jacobian1: -5.632049e-14  Jacobian2: 0  SSE: 32217.36 
lambda: 0  rho: 1.490116e-08  function: -143439.3  Jacobian1: 0  Jacobian2: -5.632049e-14  SSE: 32217.36 
lambda: 0.7108311  rho: 0.7033628  function: -135462.5  Jacobian1: -176.2982  Jacobian2: -171.6101  SSE: 19249.57 
lambda: 0.7104842  rho: 0.7033628  function: -135455.7  Jacobian1: -176.0781  Jacobian2: -171.6101  SSE: 19247.64 
lambda: 0.7108311  rho: 0.7030159  function: -135455.8  Jacobian1: -176.2982  Jacobian2: -171.3949  SSE: 19247.62 
lambda: 1  rho: -0.2421222  function: -132467  Jacobian1: -590.3752  Jacobian2: -14.46308  SSE: 14612.42 
lambda: 0.9999982  rho: -0.2421222  function: -132055.6  Jacobian1: -567.5233  Jacobian2: -14.46308  SSE: 14612.43 
lambda: 1  rho: -0.2421203  function: -132467  Jacobian1: -590.3752  Jacobian2: -14.46287  SSE: 14612.43 
lambda: 0.710725  rho: -1.199368  function: -140586.8  Jacobian1: -176.2309  Jacobian2: -353.1569  SSE: 20587.41 
lambda: 0.7107436  rho: -0.6498819  function: -131922.2  Jacobian1: -176.2426  Jacobian2: -100.7608  SSE: 17736.08 
lambda: 0.7500314  rho: -0.2421308  function: -128579.9  Jacobian1: -202.7657  Jacobian2: -14.4641  SSE: 16340.24 
lambda: 0.7500314  rho: -0.2421308  function: -128579.9  Jacobian1: -202.7657  Jacobian2: -14.4641  SSE: 16340.24 
lambda: 0.7500314  rho: -0.2419146  function: -128579.6  Jacobian1: -202.7657  Jacobian2: -14.43871  SSE: 16340.35 
lambda: 0.7500378  rho: -0.1171465  function: -128567.4  Jacobian1: -202.7703  Jacobian2: -3.453915  SSE: 16450.27 
lambda: 0.7500377  rho: -0.1171465  function: -128567.4  Jacobian1: -202.7703  Jacobian2: -3.453915  SSE: 16450.27 
lambda: 0.7500378  rho: -0.117396  function: -128567.1  Jacobian1: -202.7703  Jacobian2: -3.468488  SSE: 16449.95 
lambda: 0.7501855  rho: -0.1746336  function: -128536.4  Jacobian1: -202.8764  Jacobian2: -7.600774  SSE: 16386.4 
lambda: 0.7501855  rho: -0.1746336  function: -128536.4  Jacobian1: -202.8763  Jacobian2: -7.600774  SSE: 16386.4 
lambda: 0.7501855  rho: -0.1746311  function: -128536.4  Jacobian1: -202.8764  Jacobian2: -7.60056  SSE: 16386.4 
lambda: 0.7503398  rho: -0.1758926  function: -128535.3  Jacobian1: -202.9872  Jacobian2: -7.709203  SSE: 16383.44 
lambda: 0.7503397  rho: -0.1758926  function: -128535.3  Jacobian1: -202.9872  Jacobian2: -7.709203  SSE: 16383.44 
lambda: 0.7503398  rho: -0.1758752  function: -128535.3  Jacobian1: -202.9872  Jacobian2: -7.707695  SSE: 16383.46 
lambda: 0.7503398  rho: -0.1759101  function: -128535.3  Jacobian1: -202.9872  Jacobian2: -7.710711  SSE: 16383.43 
lambda: 0.7520442  rho: -0.1812726  function: -128523.8  Jacobian1: -204.2155  Jacobian2: -8.180982  SSE: 16358.5 
lambda: 0.7520441  rho: -0.1812726  function: -128523.8  Jacobian1: -204.2154  Jacobian2: -8.180982  SSE: 16358.5 
lambda: 0.7520442  rho: -0.181271  function: -128523.8  Jacobian1: -204.2155  Jacobian2: -8.180836  SSE: 16358.5 
lambda: 0.7520442  rho: -0.1812743  function: -128523.8  Jacobian1: -204.2155  Jacobian2: -8.181128  SSE: 16358.49 
lambda: 0.7693085  rho: -0.2267794  function: -128405.2  Jacobian1: -217.0497  Jacobian2: -12.71593  SSE: 16105.34 
lambda: 0.769308  rho: -0.2267794  function: -128405.2  Jacobian1: -217.0493  Jacobian2: -12.71593  SSE: 16105.35 
lambda: 0.7693085  rho: -0.2267818  function: -128405.2  Jacobian1: -217.0497  Jacobian2: -12.71619  SSE: 16105.34 
lambda: 0.838941  rho: -0.4085873  function: -127941.3  Jacobian1: -277.4162  Jacobian2: -40.40115  SSE: 14955.03 
lambda: 0.9455844  rho: -0.6870291  function: -127928.6  Jacobian1: -414.9478  Jacobian2: -112.5182  SSE: 13040.98 
lambda: 0.9455836  rho: -0.6870291  function: -127928.6  Jacobian1: -414.9464  Jacobian2: -112.5182  SSE: 13040.99 
lambda: 0.9455844  rho: -0.6870314  function: -127928.6  Jacobian1: -414.9478  Jacobian2: -112.5189  SSE: 13040.97 
lambda: 0.8389201  rho: -0.412414  function: -127941.1  Jacobian1: -277.3956  Jacobian2: -41.14705  SSE: 14947.86 
lambda: 0.8879622  rho: -0.5532162  function: -127735.1  Jacobian1: -331.1665  Jacobian2: -73.29553  SSE: 14029.73 
lambda: 0.8879629  rho: -0.5532162  function: -127735.1  Jacobian1: -331.1673  Jacobian2: -73.29553  SSE: 14029.72 
lambda: 0.8879622  rho: -0.5532139  function: -127735.1  Jacobian1: -331.1665  Jacobian2: -73.29493  SSE: 14029.73 
lambda: 0.8967247  rho: -0.5759109  function: -127723.4  Jacobian1: -342.1471  Jacobian2: -79.34319  SSE: 13869.19 
lambda: 0.8967242  rho: -0.5759109  function: -127723.4  Jacobian1: -342.1464  Jacobian2: -79.34319  SSE: 13869.2 
lambda: 0.8967247  rho: -0.5759069  function: -127723.4  Jacobian1: -342.1471  Jacobian2: -79.34209  SSE: 13869.2 
lambda: 0.8967247  rho: -0.575915  function: -127723.4  Jacobian1: -342.1471  Jacobian2: -79.34429  SSE: 13869.18 
lambda: 0.9020985  rho: -0.5920369  function: -127721.7  Jacobian1: -349.135  Jacobian2: -83.7888  SSE: 13765.46 
lambda: 0.9020979  rho: -0.5920369  function: -127721.7  Jacobian1: -349.1342  Jacobian2: -83.7888  SSE: 13765.47 
lambda: 0.9020985  rho: -0.5920346  function: -127721.7  Jacobian1: -349.135  Jacobian2: -83.78814  SSE: 13765.47 
lambda: 0.901471  rho: -0.5906629  function: -127721.6  Jacobian1: -348.3085  Jacobian2: -83.40517  SSE: 13776.3 
lambda: 0.9014716  rho: -0.5906629  function: -127721.6  Jacobian1: -348.3093  Jacobian2: -83.40517  SSE: 13776.29 
lambda: 0.901471  rho: -0.5906592  function: -127721.6  Jacobian1: -348.3085  Jacobian2: -83.40415  SSE: 13776.31 
lambda: 0.901471  rho: -0.5906666  function: -127721.6  Jacobian1: -348.3085  Jacobian2: -83.4062  SSE: 13776.29 
lambda: 0.9014897  rho: -0.5908307  function: -127721.6  Jacobian1: -348.3331  Jacobian2: -83.45198  SSE: 13775.66 
lambda: 0.9014964  rho: -0.5908307  function: -127721.6  Jacobian1: -348.342  Jacobian2: -83.45198  SSE: 13775.58 
lambda: 0.9014829  rho: -0.5908307  function: -127721.6  Jacobian1: -348.3241  Jacobian2: -83.45198  SSE: 13775.74 
lambda: 0.9014897  rho: -0.5908192  function: -127721.6  Jacobian1: -348.3331  Jacobian2: -83.44878  SSE: 13775.69 
lambda: 0.9014897  rho: -0.5908421  function: -127721.6  Jacobian1: -348.3331  Jacobian2: -83.45517  SSE: 13775.63 
lambda: 0.9014921  rho: -0.5908689  function: -127721.6  Jacobian1: -348.3362  Jacobian2: -83.46264  SSE: 13775.53 
lambda: 0.9015031  rho: -0.5908689  function: -127721.6  Jacobian1: -348.3507  Jacobian2: -83.46264  SSE: 13775.4 
lambda: 0.9014811  rho: -0.5908689  function: -127721.6  Jacobian1: -348.3218  Jacobian2: -83.46264  SSE: 13775.66 
lambda: 0.9014921  rho: -0.5908331  function: -127721.6  Jacobian1: -348.3362  Jacobian2: -83.45264  SSE: 13775.62 
lambda: 0.9014921  rho: -0.5909048  function: -127721.6  Jacobian1: -348.3362  Jacobian2: -83.47264  SSE: 13775.44 
lambda: 0.9014921  rho: -0.5908689  function: -127721.6  Jacobian1: -348.3362  Jacobian2: -83.46264  SSE: 13775.53 
lambda: 0.8  rho: 0.8  function: -139571.4  Jacobian1: -241.7962  Jacobian2: -241.7962  SSE: 20445.59 
lambda: 0.8  rho: 0.8  function: -139571.4  Jacobian1: -241.7962  Jacobian2: -241.7962  SSE: 20445.59 
lambda: 0.8  rho: 0.8  function: -139571.4  Jacobian1: -241.7962  Jacobian2: -241.7962  SSE: 20445.59 
lambda: 0.09283866  rho: 0.09294779  function: -138371.7  Jacobian1: -2.270443  Jacobian2: -2.275847  SSE: 26742.54 
lambda: 0.09318923  rho: 0.09294779  function: -138362.9  Jacobian1: -2.287828  Jacobian2: -2.275847  SSE: 26733.73 
lambda: 0.09283866  rho: 0.09329835  function: -138363  Jacobian1: -2.270443  Jacobian2: -2.293254  SSE: 26733.82 
lambda: 0.4708159  rho: 0.4202598  function: -129674.3  Jacobian1: -66.78193  Jacobian2: -51.99098  SSE: 18125.39 
lambda: 0.4708141  rho: 0.4202598  function: -129674.3  Jacobian1: -66.78136  Jacobian2: -51.99098  SSE: 18125.39 
lambda: 0.4708159  rho: 0.420258  function: -129674.3  Jacobian1: -66.78193  Jacobian2: -51.99049  SSE: 18125.39 
lambda: 0.7998744  rho: 0.043802  function: -129239.2  Jacobian1: -241.6894  Jacobian2: -0.4993093  SSE: 16464.84 
lambda: 0.7998762  rho: 0.043802  function: -129239.2  Jacobian1: -241.6909  Jacobian2: -0.4993093  SSE: 16464.83 
lambda: 0.7998744  rho: 0.04380373  function: -129239.2  Jacobian1: -241.6894  Jacobian2: -0.4993491  SSE: 16464.84 
lambda: 1  rho: -0.542533  function: -130696.3  Jacobian1: -590.3752  Jacobian2: -70.53276  SSE: 13214.64 
lambda: 0.7961005  rho: -0.1328145  function: -128448.5  Jacobian1: -238.5035  Jacobian2: -4.427299  SSE: 15992.9 
lambda: 0.7961018  rho: -0.1328145  function: -128448.5  Jacobian1: -238.5046  Jacobian2: -4.427299  SSE: 15992.89 
lambda: 0.7961005  rho: -0.1328125  function: -128448.5  Jacobian1: -238.5035  Jacobian2: -4.427168  SSE: 15992.91 
lambda: 0.8182767  rho: -0.3080738  function: -128090  Jacobian1: -257.8639  Jacobian2: -23.21505  SSE: 15399.62 
lambda: 0.8182778  rho: -0.3080738  function: -128090  Jacobian1: -257.8649  Jacobian2: -23.21505  SSE: 15399.61 
lambda: 0.8182767  rho: -0.3080713  function: -128090  Jacobian1: -257.8639  Jacobian2: -23.21467  SSE: 15399.63 
lambda: 0.9991042  rho: -0.6116062  function: -129834  Jacobian1: -555.5822  Jacobian2: -89.35024  SSE: 12942.77 
lambda: 0.8800595  rho: -0.3289181  function: -128075  Jacobian1: -321.6689  Jacobian2: -26.39802  SSE: 14734.42 
lambda: 0.8800584  rho: -0.3289181  function: -128074.9  Jacobian1: -321.6676  Jacobian2: -26.39802  SSE: 14734.43 
lambda: 0.8800595  rho: -0.3289154  function: -128075  Jacobian1: -321.6689  Jacobian2: -26.3976  SSE: 14734.43 
lambda: 0.8514872  rho: -0.3446192  function: -127963  Jacobian1: -290.0968  Jacobian2: -28.92737  SSE: 14955.01 
lambda: 0.851488  rho: -0.3446192  function: -127963  Jacobian1: -290.0977  Jacobian2: -28.92737  SSE: 14955 
lambda: 0.8514872  rho: -0.3446157  function: -127963  Jacobian1: -290.0968  Jacobian2: -28.9268  SSE: 14955.02 
lambda: 0.8671484  rho: -0.4079147  function: -127859.7  Jacobian1: -306.9002  Jacobian2: -40.27075  SSE: 14628.71 
lambda: 0.914132  rho: -0.5978014  function: -127741.1  Jacobian1: -365.561  Jacobian2: -85.40799  SSE: 13614.27 
lambda: 0.9141313  rho: -0.5978014  function: -127741.1  Jacobian1: -365.5599  Jacobian2: -85.40799  SSE: 13614.27 
lambda: 0.914132  rho: -0.5977981  function: -127741.1  Jacobian1: -365.561  Jacobian2: -85.40705  SSE: 13614.28 
lambda: 0.8112129  rho: -0.8374538  function: -130293.6  Jacobian1: -251.5258  Jacobian2: -167.2782  SSE: 15246.93 
lambda: 0.8882542  rho: -0.601056  function: -127752  Jacobian1: -331.5245  Jacobian2: -86.32919  SSE: 13916.42 
lambda: 0.9025995  rho: -0.5992518  function: -127722  Jacobian1: -349.7969  Jacobian2: -85.8179  SSE: 13741.52 
lambda: 0.9026012  rho: -0.5992518  function: -127722  Jacobian1: -349.7992  Jacobian2: -85.8179  SSE: 13741.5 
lambda: 0.9025995  rho: -0.5992463  function: -127722  Jacobian1: -349.7969  Jacobian2: -85.81634  SSE: 13741.53 
lambda: 0.9009917  rho: -0.5877402  function: -127721.7  Jacobian1: -347.6791  Jacobian2: -82.59219  SSE: 13789.26 
lambda: 0.9009922  rho: -0.5877402  function: -127721.7  Jacobian1: -347.6798  Jacobian2: -82.59219  SSE: 13789.26 
lambda: 0.9009911  rho: -0.5877402  function: -127721.7  Jacobian1: -347.6784  Jacobian2: -82.59219  SSE: 13789.27 
lambda: 0.9009917  rho: -0.5877436  function: -127721.7  Jacobian1: -347.6791  Jacobian2: -82.59314  SSE: 13789.25 
lambda: 0.9014822  rho: -0.5908463  function: -127721.6  Jacobian1: -348.3232  Jacobian2: -83.45633  SSE: 13775.71 
lambda: 0.9014893  rho: -0.5908463  function: -127721.6  Jacobian1: -348.3325  Jacobian2: -83.45633  SSE: 13775.62 
lambda: 0.9014751  rho: -0.5908463  function: -127721.6  Jacobian1: -348.3139  Jacobian2: -83.45633  SSE: 13775.79 
lambda: 0.9014822  rho: -0.5908436  function: -127721.6  Jacobian1: -348.3232  Jacobian2: -83.45559  SSE: 13775.71 
lambda: 0.901492  rho: -0.5908711  function: -127721.6  Jacobian1: -348.3361  Jacobian2: -83.46325  SSE: 13775.53 
lambda: 0.9015012  rho: -0.5908711  function: -127721.6  Jacobian1: -348.3482  Jacobian2: -83.46325  SSE: 13775.42 
lambda: 0.9014828  rho: -0.5908711  function: -127721.6  Jacobian1: -348.324  Jacobian2: -83.46325  SSE: 13775.64 
lambda: 0.901492  rho: -0.5908149  function: -127721.6  Jacobian1: -348.3361  Jacobian2: -83.44756  SSE: 13775.67 
lambda: 0.901492  rho: -0.5909274  function: -127721.6  Jacobian1: -348.3361  Jacobian2: -83.47895  SSE: 13775.39 
lambda: 0.901492  rho: -0.5908711  function: -127721.6  Jacobian1: -348.3361  Jacobian2: -83.46325  SSE: 13775.53 
lambda: 0.8  rho: -1.130119  function: -134272.5  Jacobian1: -241.7962  Jacobian2: -310.7723  SSE: 16138.49 
lambda: 0.8  rho: -1.130119  function: -134272.5  Jacobian1: -241.7962  Jacobian2: -310.7723  SSE: 16138.49 
lambda: 0.8  rho: -1.130119  function: -134272.5  Jacobian1: -241.7962  Jacobian2: -310.7723  SSE: 16138.49 
lambda: 1  rho: -0.2781232  function: -132196.8  Jacobian1: -590.3752  Jacobian2: -18.99152  SSE: 14427.72 
lambda: 0.9996573  rho: -0.2781232  function: -131648.9  Jacobian1: -559.9153  Jacobian2: -18.99152  SSE: 14427.92 
lambda: 1  rho: -0.2784659  function: -132194.3  Jacobian1: -590.3752  Jacobian2: -19.03751  SSE: 14425.98 
lambda: 0.7895168  rho: 0.1055055  function: -129542.2  Jacobian1: -233.0463  Jacobian2: -2.941942  SSE: 16713.61 
lambda: 0.7895169  rho: 0.1055055  function: -129542.2  Jacobian1: -233.0464  Jacobian2: -2.941942  SSE: 16713.6 
lambda: 0.7895168  rho: 0.1055143  function: -129542.3  Jacobian1: -233.0463  Jacobian2: -2.942445  SSE: 16713.64 
lambda: 0.7877612  rho: -0.1132763  function: -128500.2  Jacobian1: -231.6121  Jacobian2: -3.231721  SSE: 16107.45 
lambda: 0.7877613  rho: -0.1132763  function: -128500.2  Jacobian1: -231.6122  Jacobian2: -3.231721  SSE: 16107.44 
lambda: 0.7877612  rho: -0.1129681  function: -128501.1  Jacobian1: -231.6121  Jacobian2: -3.21434  SSE: 16108.13 
lambda: 0.8258984  rho: -0.3287155  function: -128041.3  Jacobian1: -264.8942  Jacobian2: -26.36613  SSE: 15270.98 
lambda: 0.9413767  rho: -0.5781074  function: -127988.7  Jacobian1: -407.6739  Jacobian2: -79.94146  SSE: 13412.89 
lambda: 0.9413778  rho: -0.5781074  function: -127988.8  Jacobian1: -407.6758  Jacobian2: -79.94146  SSE: 13412.88 
lambda: 0.9413767  rho: -0.5781049  function: -127988.7  Jacobian1: -407.6739  Jacobian2: -79.94077  SSE: 13412.9 
lambda: 0.8107339  rho: -1.049911  function: -132632.9  Jacobian1: -251.1019  Jacobian2: -266.0438  SSE: 15564.1 
lambda: 0.8539825  rho: -0.5895475  function: -128001.2  Jacobian1: -292.6986  Jacobian2: -83.09442  SSE: 14432.31 
lambda: 0.8981275  rho: -0.5837688  function: -127722.6  Jacobian1: -343.9519  Jacobian2: -81.49401  SSE: 13833.09 
lambda: 0.8981287  rho: -0.5837688  function: -127722.6  Jacobian1: -343.9534  Jacobian2: -81.49401  SSE: 13833.07 
lambda: 0.8981275  rho: -0.5837607  function: -127722.6  Jacobian1: -343.9519  Jacobian2: -81.49177  SSE: 13833.11 
lambda: 0.9032025  rho: -0.608863  function: -127723.3  Jacobian1: -350.5959  Jacobian2: -88.55958  SSE: 13710.53 
lambda: 0.9007353  rho: -0.5930868  function: -127721.8  Jacobian1: -347.3431  Jacobian2: -84.08252  SSE: 13778.98 
lambda: 0.9007347  rho: -0.5930868  function: -127721.8  Jacobian1: -347.3423  Jacobian2: -84.08252  SSE: 13778.98 
lambda: 0.9007353  rho: -0.5930909  function: -127721.8  Jacobian1: -347.3431  Jacobian2: -84.08365  SSE: 13778.97 
lambda: 0.9023481  rho: -0.5836243  function: -127722.4  Jacobian1: -349.4646  Jacobian2: -81.4542  SSE: 13783.82 
lambda: 0.9025296  rho: -0.5904958  function: -127721.8  Jacobian1: -349.7044  Jacobian2: -83.35859  SSE: 13764.28 
lambda: 0.9025287  rho: -0.5904958  function: -127721.8  Jacobian1: -349.7032  Jacobian2: -83.35859  SSE: 13764.3 
lambda: 0.9025296  rho: -0.5905002  function: -127721.8  Jacobian1: -349.7044  Jacobian2: -83.35981  SSE: 13764.27 
lambda: 0.9014317  rho: -0.5905633  function: -127721.6  Jacobian1: -348.2568  Jacobian2: -83.37741  SSE: 13777.01 
lambda: 0.9014323  rho: -0.5905633  function: -127721.6  Jacobian1: -348.2576  Jacobian2: -83.37741  SSE: 13777 
lambda: 0.9014317  rho: -0.5905605  function: -127721.6  Jacobian1: -348.2568  Jacobian2: -83.37663  SSE: 13777.02 
lambda: 0.9015058  rho: -0.5909612  function: -127721.6  Jacobian1: -348.3543  Jacobian2: -83.48837  SSE: 13775.14 
lambda: 0.9015124  rho: -0.5909612  function: -127721.6  Jacobian1: -348.363  Jacobian2: -83.48837  SSE: 13775.06 
lambda: 0.9014993  rho: -0.5909612  function: -127721.6  Jacobian1: -348.3457  Jacobian2: -83.48837  SSE: 13775.22 
lambda: 0.9015058  rho: -0.5909491  function: -127721.6  Jacobian1: -348.3543  Jacobian2: -83.485  SSE: 13775.17 
lambda: 0.9015058  rho: -0.5909733  function: -127721.6  Jacobian1: -348.3543  Jacobian2: -83.49175  SSE: 13775.11 
lambda: 0.9014919  rho: -0.5908689  function: -127721.6  Jacobian1: -348.336  Jacobian2: -83.46263  SSE: 13775.54 
lambda: 0.9015058  rho: -0.5908689  function: -127721.6  Jacobian1: -348.3542  Jacobian2: -83.46263  SSE: 13775.37 
lambda: 0.9014781  rho: -0.5908689  function: -127721.6  Jacobian1: -348.3178  Jacobian2: -83.46263  SSE: 13775.7 
lambda: 0.9014919  rho: -0.5908425  function: -127721.6  Jacobian1: -348.336  Jacobian2: -83.45528  SSE: 13775.6 
lambda: 0.9014919  rho: -0.5908952  function: -127721.6  Jacobian1: -348.336  Jacobian2: -83.46997  SSE: 13775.47 
lambda: 0.9014919  rho: -0.5908686  function: -127721.6  Jacobian1: -348.336  Jacobian2: -83.46256  SSE: 13775.54 
lambda: 0.9015063  rho: -0.5908686  function: -127721.6  Jacobian1: -348.3549  Jacobian2: -83.46256  SSE: 13775.37 
lambda: 0.9014776  rho: -0.5908686  function: -127721.6  Jacobian1: -348.3172  Jacobian2: -83.46256  SSE: 13775.71 
lambda: 0.9014919  rho: -0.590809  function: -127721.6  Jacobian1: -348.336  Jacobian2: -83.44593  SSE: 13775.69 
lambda: 0.9014919  rho: -0.5909282  function: -127721.6  Jacobian1: -348.336  Jacobian2: -83.47919  SSE: 13775.39 
lambda: 0.9014919  rho: -0.5908686  function: -127721.6  Jacobian1: -348.336  Jacobian2: -83.46256  SSE: 13775.54 
lambda: 0.8  rho: -1.130119  function: -134272.5  Jacobian1: -241.7962  Jacobian2: -310.7723  SSE: 16138.49 
lambda: 0.8  rho: -1.130119  function: -134272.5  Jacobian1: -241.7962  Jacobian2: -310.7723  SSE: 16138.49 
lambda: 0.8  rho: -1.130119  function: -134272.5  Jacobian1: -241.7962  Jacobian2: -310.7723  SSE: 16138.49 
lambda: 1  rho: -0.2781232  function: -132196.8  Jacobian1: -590.3752  Jacobian2: -18.99152  SSE: 14427.72 
lambda: 0.9996573  rho: -0.2781232  function: -131648.9  Jacobian1: -559.9153  Jacobian2: -18.99152  SSE: 14427.92 
lambda: 1  rho: -0.2784659  function: -132194.3  Jacobian1: -590.3752  Jacobian2: -19.03751  SSE: 14425.98 
lambda: 0.7895168  rho: 0.1055055  function: -129542.2  Jacobian1: -233.0463  Jacobian2: -2.941942  SSE: 16713.61 
lambda: 0.7895169  rho: 0.1055055  function: -129542.2  Jacobian1: -233.0464  Jacobian2: -2.941942  SSE: 16713.6 
lambda: 0.7895168  rho: 0.1055143  function: -129542.3  Jacobian1: -233.0463  Jacobian2: -2.942445  SSE: 16713.64 
lambda: 0.7877612  rho: -0.1132763  function: -128500.2  Jacobian1: -231.6121  Jacobian2: -3.231721  SSE: 16107.45 
lambda: 0.7877613  rho: -0.1132763  function: -128500.2  Jacobian1: -231.6122  Jacobian2: -3.231721  SSE: 16107.44 
lambda: 0.7877612  rho: -0.1129681  function: -128501.1  Jacobian1: -231.6121  Jacobian2: -3.21434  SSE: 16108.13 
lambda: 0.8258984  rho: -0.3287155  function: -128041.3  Jacobian1: -264.8942  Jacobian2: -26.36613  SSE: 15270.98 
lambda: 0.9413767  rho: -0.5781074  function: -127988.7  Jacobian1: -407.6739  Jacobian2: -79.94146  SSE: 13412.89 
lambda: 0.9413778  rho: -0.5781074  function: -127988.8  Jacobian1: -407.6758  Jacobian2: -79.94146  SSE: 13412.88 
lambda: 0.9413767  rho: -0.5781049  function: -127988.7  Jacobian1: -407.6739  Jacobian2: -79.94077  SSE: 13412.9 
lambda: 0.8107339  rho: -1.049911  function: -132632.9  Jacobian1: -251.1019  Jacobian2: -266.0438  SSE: 15564.1 
lambda: 0.8539825  rho: -0.5895475  function: -128001.2  Jacobian1: -292.6986  Jacobian2: -83.09442  SSE: 14432.31 
lambda: 0.8981275  rho: -0.5837688  function: -127722.6  Jacobian1: -343.9519  Jacobian2: -81.49401  SSE: 13833.09 
lambda: 0.8981287  rho: -0.5837688  function: -127722.6  Jacobian1: -343.9534  Jacobian2: -81.49401  SSE: 13833.07 
lambda: 0.8981275  rho: -0.5837607  function: -127722.6  Jacobian1: -343.9519  Jacobian2: -81.49177  SSE: 13833.11 
lambda: 0.9032025  rho: -0.608863  function: -127723.3  Jacobian1: -350.5959  Jacobian2: -88.55958  SSE: 13710.53 
lambda: 0.9007353  rho: -0.5930868  function: -127721.8  Jacobian1: -347.3431  Jacobian2: -84.08252  SSE: 13778.98 
lambda: 0.9007347  rho: -0.5930868  function: -127721.8  Jacobian1: -347.3423  Jacobian2: -84.08252  SSE: 13778.98 
lambda: 0.9007353  rho: -0.5930909  function: -127721.8  Jacobian1: -347.3431  Jacobian2: -84.08365  SSE: 13778.97 
lambda: 0.9023481  rho: -0.5836243  function: -127722.4  Jacobian1: -349.4646  Jacobian2: -81.4542  SSE: 13783.82 
lambda: 0.9025296  rho: -0.5904958  function: -127721.8  Jacobian1: -349.7044  Jacobian2: -83.35859  SSE: 13764.28 
lambda: 0.9025287  rho: -0.5904958  function: -127721.8  Jacobian1: -349.7032  Jacobian2: -83.35859  SSE: 13764.3 
lambda: 0.9025296  rho: -0.5905002  function: -127721.8  Jacobian1: -349.7044  Jacobian2: -83.35981  SSE: 13764.27 
lambda: 0.9014317  rho: -0.5905633  function: -127721.6  Jacobian1: -348.2568  Jacobian2: -83.37741  SSE: 13777.01 
lambda: 0.9014323  rho: -0.5905633  function: -127721.6  Jacobian1: -348.2576  Jacobian2: -83.37741  SSE: 13777 
lambda: 0.9014317  rho: -0.5905605  function: -127721.6  Jacobian1: -348.2568  Jacobian2: -83.37663  SSE: 13777.02 
lambda: 0.9015058  rho: -0.5909612  function: -127721.6  Jacobian1: -348.3543  Jacobian2: -83.48837  SSE: 13775.14 
lambda: 0.9015124  rho: -0.5909612  function: -127721.6  Jacobian1: -348.363  Jacobian2: -83.48837  SSE: 13775.06 
lambda: 0.9014993  rho: -0.5909612  function: -127721.6  Jacobian1: -348.3457  Jacobian2: -83.48837  SSE: 13775.22 
lambda: 0.9015058  rho: -0.5909491  function: -127721.6  Jacobian1: -348.3543  Jacobian2: -83.485  SSE: 13775.17 
lambda: 0.9015058  rho: -0.5909733  function: -127721.6  Jacobian1: -348.3543  Jacobian2: -83.49175  SSE: 13775.11 
lambda: 0.9014919  rho: -0.5908689  function: -127721.6  Jacobian1: -348.336  Jacobian2: -83.46263  SSE: 13775.54 
lambda: 0.9015058  rho: -0.5908689  function: -127721.6  Jacobian1: -348.3542  Jacobian2: -83.46263  SSE: 13775.37 
lambda: 0.9014781  rho: -0.5908689  function: -127721.6  Jacobian1: -348.3178  Jacobian2: -83.46263  SSE: 13775.7 
lambda: 0.9014919  rho: -0.5908425  function: -127721.6  Jacobian1: -348.336  Jacobian2: -83.45528  SSE: 13775.6 
lambda: 0.9014919  rho: -0.5908952  function: -127721.6  Jacobian1: -348.336  Jacobian2: -83.46997  SSE: 13775.47 
lambda: 0.9014919  rho: -0.5908686  function: -127721.6  Jacobian1: -348.336  Jacobian2: -83.46256  SSE: 13775.54 
lambda: 0.9015063  rho: -0.5908686  function: -127721.6  Jacobian1: -348.3549  Jacobian2: -83.46256  SSE: 13775.37 
lambda: 0.9014776  rho: -0.5908686  function: -127721.6  Jacobian1: -348.3172  Jacobian2: -83.46256  SSE: 13775.71 
lambda: 0.9014919  rho: -0.590809  function: -127721.6  Jacobian1: -348.336  Jacobian2: -83.44593  SSE: 13775.69 
lambda: 0.9014919  rho: -0.5909282  function: -127721.6  Jacobian1: -348.336  Jacobian2: -83.47919  SSE: 13775.39 
lambda: 0.9014919  rho: -0.5908686  function: -127721.6  Jacobian1: -348.336  Jacobian2: -83.46256  SSE: 13775.54 
# sp_err_mines$logLik
# sp_lag_mines$logLik
# sp_lag_mines_unadjll$logLik
# sp_err_lag_mines$logLik

# summary(sp_err_mines)
# summary(sp_lag_mines)
# summary(sp_lag_mines_unadjll)
# summary(sp_err_lag_mines)

sparse.W <- listw2dgCMatrix(fmatlw)
time <- length(unique(allcomp_full$year))
s.lwcounties <- kronecker(Matrix::Diagonal(time), sparse.W)
trMatc <- spatialreg::trW(s.lwcounties, type="mult")
implag <- spatialreg::impacts(sp_lag_mines, tr = trMatc, R = 200)
imperrlag <- spatialreg::impacts(sp_err_lag_mines, tr = trMatc, R = 200)

summary(sp_err_mines)
Spatial panel fixed effects error model
 

Call:
spml(formula = fmdiff_uer, data = allcomp_full, index = NULL, 
    listw = fmatlw, na.action = na.fail, model = "within", effect = "twoways", 
    lag = FALSE, spatial.error = "b", quiet = FALSE)

Residuals:
      Min.    1st Qu.     Median    3rd Qu.       Max. 
-8.7738952 -0.3734312  0.0050779  0.3656829 10.1785341 

Spatial error parameter:
     Estimate Std. Error t-value  Pr(>|t|)    
rho 0.7422315  0.0036162  205.25 < 2.2e-16 ***

Coefficients:
                      Estimate Std. Error  t-value  Pr(>|t|)    
mines_diff          -0.0255521  0.0042560  -6.0037 1.928e-09 ***
lag_diff            -0.0093789  0.0041154  -2.2790   0.02267 *  
lag_diff2            0.0205106  0.0040036   5.1230 3.007e-07 ***
diff_log_realgdp_pc -0.5434661  0.0300190 -18.1041 < 2.2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(implag, zstats=TRUE, short=TRUE)
Impact measures (lag, trace):
                          Direct    Indirect       Total
mines_diff          -0.035802540 -0.08197462 -0.11777716
lag_diff            -0.008906544 -0.02039270 -0.02929925
lag_diff2            0.027806380  0.06366636  0.09147274
diff_log_realgdp_pc -0.650749889 -1.48997733 -2.14072722
========================================================
Simulation results ( variance matrix):
========================================================
Simulated standard errors
                         Direct    Indirect      Total
mines_diff          0.004452784 0.010248575 0.01467281
lag_diff            0.004533657 0.010363744 0.01489543
lag_diff2           0.004106934 0.009485079 0.01357497
diff_log_realgdp_pc 0.030339533 0.073921998 0.10301104

Simulated z-values:
                        Direct   Indirect      Total
mines_diff           -7.978939  -7.953539  -7.976725
lag_diff             -2.053593  -2.058539  -2.057305
lag_diff2             6.738775   6.694713   6.716450
diff_log_realgdp_pc -21.366461 -20.120401 -20.731647

Simulated p-values:
                    Direct     Indirect   Total     
mines_diff          1.5543e-15 1.7764e-15 1.5543e-15
lag_diff            0.040015   0.039538   0.039657  
lag_diff2           1.5973e-11 2.1610e-11 1.8620e-11
diff_log_realgdp_pc < 2.22e-16 < 2.22e-16 < 2.22e-16
summary(imperrlag, zstats=TRUE, short=TRUE)
Impact measures (lag, trace):
                          Direct    Indirect       Total
mines_diff          -0.035672461 -0.21468589 -0.25035835
lag_diff            -0.007526441 -0.04529603 -0.05282247
lag_diff2            0.027054545  0.16282110  0.18987564
diff_log_realgdp_pc -0.549659757 -3.30799147 -3.85765123
========================================================
Simulation results ( variance matrix):
========================================================
Simulated standard errors
                         Direct   Indirect      Total
mines_diff          0.004650505 0.02829550 0.03289168
lag_diff            0.004126668 0.02491662 0.02904013
lag_diff2           0.004218824 0.02580231 0.02998923
diff_log_realgdp_pc 0.032521515 0.21763109 0.24861457

Simulated z-values:
                        Direct   Indirect      Total
mines_diff           -7.672573  -7.591105  -7.615161
lag_diff             -1.798661  -1.794389  -1.795192
lag_diff2             6.447663   6.346919   6.367845
diff_log_realgdp_pc -16.969292 -15.267935 -15.584945

Simulated p-values:
                    Direct     Indirect   Total     
mines_diff          1.6875e-14 3.1752e-14 2.6423e-14
lag_diff            0.072072   0.072751   0.072623  
lag_diff2           1.1359e-10 2.1967e-10 1.9170e-10
diff_log_realgdp_pc < 2.22e-16 < 2.22e-16 < 2.22e-16
mainfull <- feols(FE_diffuer, allcomp_full, se = 'twoway')
merged <- allcomp_full %>%
   mutate(residuals_lag = residuals(sp_lag_mines), residuals_lagerr = residuals(sp_err_lag_mines), residuals_err = residuals(sp_err_mines), residuals_main = residuals(mainfull))

#moran.mc(merged$residuals_lag, listw = fmatlw, 1000, zero.policy = TRUE)

for(i in 2002:2019){
  print(moran.mc(merged$residuals_lag[which(merged$year == i)], listw = fmatlw, 1000, zero.policy = TRUE))
}

    Monte-Carlo simulation of Moran I

data:  merged$residuals_lag[which(merged$year == i)] 
weights: fmatlw  
number of simulations + 1: 1001 

statistic = 0.0061992, observed rank = 723, p-value = 0.2777
alternative hypothesis: greater


    Monte-Carlo simulation of Moran I

data:  merged$residuals_lag[which(merged$year == i)] 
weights: fmatlw  
number of simulations + 1: 1001 

statistic = -0.010425, observed rank = 169, p-value = 0.8312
alternative hypothesis: greater


    Monte-Carlo simulation of Moran I

data:  merged$residuals_lag[which(merged$year == i)] 
weights: fmatlw  
number of simulations + 1: 1001 

statistic = 0.017103, observed rank = 963, p-value = 0.03796
alternative hypothesis: greater


    Monte-Carlo simulation of Moran I

data:  merged$residuals_lag[which(merged$year == i)] 
weights: fmatlw  
number of simulations + 1: 1001 

statistic = -0.0025806, observed rank = 419, p-value = 0.5814
alternative hypothesis: greater


    Monte-Carlo simulation of Moran I

data:  merged$residuals_lag[which(merged$year == i)] 
weights: fmatlw  
number of simulations + 1: 1001 

statistic = -0.016705, observed rank = 54, p-value = 0.9461
alternative hypothesis: greater


    Monte-Carlo simulation of Moran I

data:  merged$residuals_lag[which(merged$year == i)] 
weights: fmatlw  
number of simulations + 1: 1001 

statistic = -0.0048322, observed rank = 353, p-value = 0.6474
alternative hypothesis: greater


    Monte-Carlo simulation of Moran I

data:  merged$residuals_lag[which(merged$year == i)] 
weights: fmatlw  
number of simulations + 1: 1001 

statistic = 0.0062847, observed rank = 747, p-value = 0.2537
alternative hypothesis: greater


    Monte-Carlo simulation of Moran I

data:  merged$residuals_lag[which(merged$year == i)] 
weights: fmatlw  
number of simulations + 1: 1001 

statistic = -0.0030046, observed rank = 377, p-value = 0.6234
alternative hypothesis: greater


    Monte-Carlo simulation of Moran I

data:  merged$residuals_lag[which(merged$year == i)] 
weights: fmatlw  
number of simulations + 1: 1001 

statistic = 0.0078975, observed rank = 768, p-value = 0.2328
alternative hypothesis: greater


    Monte-Carlo simulation of Moran I

data:  merged$residuals_lag[which(merged$year == i)] 
weights: fmatlw  
number of simulations + 1: 1001 

statistic = 0.017114, observed rank = 947, p-value = 0.05395
alternative hypothesis: greater


    Monte-Carlo simulation of Moran I

data:  merged$residuals_lag[which(merged$year == i)] 
weights: fmatlw  
number of simulations + 1: 1001 

statistic = 0.00071451, observed rank = 516, p-value = 0.4845
alternative hypothesis: greater


    Monte-Carlo simulation of Moran I

data:  merged$residuals_lag[which(merged$year == i)] 
weights: fmatlw  
number of simulations + 1: 1001 

statistic = -0.008323, observed rank = 232, p-value = 0.7682
alternative hypothesis: greater


    Monte-Carlo simulation of Moran I

data:  merged$residuals_lag[which(merged$year == i)] 
weights: fmatlw  
number of simulations + 1: 1001 

statistic = -0.044618, observed rank = 1, p-value = 0.999
alternative hypothesis: greater


    Monte-Carlo simulation of Moran I

data:  merged$residuals_lag[which(merged$year == i)] 
weights: fmatlw  
number of simulations + 1: 1001 

statistic = 0.018287, observed rank = 973, p-value = 0.02797
alternative hypothesis: greater


    Monte-Carlo simulation of Moran I

data:  merged$residuals_lag[which(merged$year == i)] 
weights: fmatlw  
number of simulations + 1: 1001 

statistic = -0.017559, observed rank = 51, p-value = 0.9491
alternative hypothesis: greater


    Monte-Carlo simulation of Moran I

data:  merged$residuals_lag[which(merged$year == i)] 
weights: fmatlw  
number of simulations + 1: 1001 

statistic = 0.019592, observed rank = 968, p-value = 0.03297
alternative hypothesis: greater


    Monte-Carlo simulation of Moran I

data:  merged$residuals_lag[which(merged$year == i)] 
weights: fmatlw  
number of simulations + 1: 1001 

statistic = -0.0070236, observed rank = 262, p-value = 0.7383
alternative hypothesis: greater


    Monte-Carlo simulation of Moran I

data:  merged$residuals_lag[which(merged$year == i)] 
weights: fmatlw  
number of simulations + 1: 1001 

statistic = 0.0065969, observed rank = 750, p-value = 0.2507
alternative hypothesis: greater
mergedp <- pdata.frame(merged, index = c("fips", "year"))
#moran.mc(mergedp$residuals_lag, listw = fmatlw, 1000, zero.policy = TRUE)
# PCD cross-sectional dependence of residuals: 
# Main: reject independence: p-value: 2.2e-16
# rho: 0.0047673
pcdtest(mergedp$residuals_main, test = "cd")

    Pesaran CD test for cross-sectional dependence in panels

data:  mergedp$residuals_main
z = 43.928, p-value < 2.2e-16
alternative hypothesis: cross-sectional dependence
pcdtest(mergedp$residuals_main, test = "rho")

    Average correlation coefficient for cross-sectional dependence in
    panels

data:  mergedp$residuals_main
rho = 0.0047673
alternative hypothesis: cross-sectional dependence
# SAR: reject independence: p-value: 0.01644
# rho: -0.00026
pcdtest(mergedp$residuals_err,listw = fmatlw, test = "cd")

    Pesaran CD test for cross-sectional dependence in panels

data:  mergedp$residuals_err
z = -2.399, p-value = 0.01644
alternative hypothesis: cross-sectional dependence
pcdtest(mergedp$residuals_err,listw = fmatlw, test = "rho")

    Average correlation coefficient for cross-sectional dependence in
    panels

data:  mergedp$residuals_err
rho = -0.00026035
alternative hypothesis: cross-sectional dependence
# SLM: reject independence: p-value: 0.036
# rho: -0.00027
pcdtest(mergedp$residuals_lag, listw = fmatlw,test = "cd")

    Pesaran CD test for cross-sectional dependence in panels

data:  mergedp$residuals_lag
z = -2.0979, p-value = 0.03591
alternative hypothesis: cross-sectional dependence
pcdtest(mergedp$residuals_lag, listw = fmatlw,test = "rho")

    Average correlation coefficient for cross-sectional dependence in
    panels

data:  mergedp$residuals_lag
rho = -0.00022767
alternative hypothesis: cross-sectional dependence
# SARAR: reject independence: p-value: 0.0425
# rho: -0.00022
pcdtest(mergedp$residuals_lagerr, listw = fmatlw, test = "cd")

    Pesaran CD test for cross-sectional dependence in panels

data:  mergedp$residuals_lagerr
z = -2.0291, p-value = 0.04245
alternative hypothesis: cross-sectional dependence
pcdtest(mergedp$residuals_lagerr, listw = fmatlw, test = "rho")

    Average correlation coefficient for cross-sectional dependence in
    panels

data:  mergedp$residuals_lagerr
rho = -0.00022021
alternative hypothesis: cross-sectional dependence
ssr_spatial <- merged %>% 
  group_by(fips) %>%
  summarise(ssr_err = sum(residuals_err^2), ssr_lag = sum(residuals_lag^2), ssr_errlag = sum(residuals_lagerr^2), ssr_main = sum(residuals_main^2))

moran.mc(ssr_spatial$ssr_main, listw = fmatlw, 1000, zero.policy = TRUE)

    Monte-Carlo simulation of Moran I

data:  ssr_spatial$ssr_main 
weights: fmatlw  
number of simulations + 1: 1001 

statistic = 0.26559, observed rank = 1001, p-value = 0.000999
alternative hypothesis: greater
moran.mc(ssr_spatial$ssr_err, listw = fmatlw, 1000, zero.policy = TRUE)

    Monte-Carlo simulation of Moran I

data:  ssr_spatial$ssr_err 
weights: fmatlw  
number of simulations + 1: 1001 

statistic = 0.30496, observed rank = 1001, p-value = 0.000999
alternative hypothesis: greater
moran.mc(ssr_spatial$ssr_lag, listw = fmatlw, 1000, zero.policy = TRUE)

    Monte-Carlo simulation of Moran I

data:  ssr_spatial$ssr_lag 
weights: fmatlw  
number of simulations + 1: 1001 

statistic = 0.27423, observed rank = 1001, p-value = 0.000999
alternative hypothesis: greater
moran.mc(ssr_spatial$ssr_errlag, listw = fmatlw, 1000, zero.policy = TRUE)

    Monte-Carlo simulation of Moran I

data:  ssr_spatial$ssr_errlag 
weights: fmatlw  
number of simulations + 1: 1001 

statistic = 0.27003, observed rank = 1001, p-value = 0.000999
alternative hypothesis: greater
ssr_spatial <- df_va(ssr_spatial)

for (i in 1:length(names(ssr_spatial)[-1])){
  mod = names(ssr_spatial[i+1])
  k <- plot_usmap(data = ssr_spatial, values = mod, regions = "counties", col = "black", size = 0.07, exclude = c("AK", "HI" )) +
  labs(title = paste0("Geographical Distribution of SSR in ",mod,"model")) +
  scale_fill_viridis(name = "ssr",  na.value = "seashell2") +
  theme(panel.background = element_rect(colour = "black", fill = "gray90"))
  print(k)
}

sp_err <- dw_compat(sp_err_mines)
sp_lag <- dw_compat_imp(implag)
sp_lag$model <- "SLM"
sp_errlag <- dw_compat_imp(imperrlag)
sp_errlag$model <- "SARAR"
spat_models <- rbind(sp_err[2:4,], sp_lag[1:3,], sp_errlag[1:3,])
  
# Spatial Durbin model here!
# library(spatialreg)
# lagsarlm(fmdiff_uer, allcomp_LA, listw = fmatlw, na.action = na.fail, Durbin = TRUE)

4.1.2 Employed overall

fmdiff_employed <- diff_log_employed ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp + diff_log_pop

emp_spat <- spml(fmdiff_employed, data = allcomp_full, index = NULL,  listw = fmatlw, 
                         lag = TRUE, na.action = na.fail, spatial.error = "b",
                         model = "within", effect = "twoways", quiet = FALSE)


impemp_spat <- spatialreg::impacts(emp_spat, tr = trMatc, R = 200)
summary(impemp_spat, zstats=TRUE, short=TRUE)

4.1.3 Unemployed spatial

fmdiff_unemp <- diff_log_unemployed ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp + diff_log_pop

unemp_spat <- spml(fmdiff_unemp, data = allcomp_full, index = NULL,  listw = fmatlw, 
                         lag = TRUE, na.action = na.fail, spatial.error = "b",
                         model = "within", effect = "twoways", quiet = FALSE)

imp_unemp_spat <- spatialreg::impacts(unemp_spat, tr = trMatc, R = 200)
summary(imp_unemp_spat, zstats=TRUE, short=TRUE)

4.1.4 Labour force spatial

fmdiff_lf <- diff_log_lf ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp + diff_log_pop
lf_spat <- spml(fmdiff_lf, data = allcomp_full, index = NULL,  listw = fmatlw, 
                         lag = TRUE, na.action = na.fail, spatial.error = "b",
                         model = "within", effect = "twoways", quiet = FALSE)

imp_lf_spat <- spatialreg::impacts(lf_spat, tr = trMatc, R = 200)
summary(imp_lf_spat, zstats=TRUE, short=TRUE)

4.1.5 Population spatial

fmdiff_pop <- diff_log_pop ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp
pop_spat <- spml(fmdiff_pop, data = allcomp_full, index = NULL,  listw = fmatlw, 
                         lag = TRUE, na.action = na.fail, spatial.error = "b",
                         model = "within", effect = "twoways", quiet = FALSE)

imp_pop_spat <- spatialreg::impacts(pop_spat, tr = trMatc, R = 200)
summary(imp_pop_spat, zstats=TRUE, short=TRUE)

4.1.6 Table of calculated impacts of overall economy spatial models (SARAR)

tab2 <- cbind(spat_output(impemp_spat)[-4], 
      spat_output(imp_unemp_spat)[-4],
      spat_output(imp_lf_spat)[-4],
      spat_output(imp_pop_spat)[-4])

kable(tab2[,1:6], booktabs=TRUE, format = "latex") %>%
  add_header_above(setNames(c("", 3, 3), c("", "Employed Persons", "Unemployed Persons"))) %>%
  kable_styling(position="center")

kable(tab2[,7:12], booktabs=TRUE, format = "latex") %>%
  add_header_above(setNames(c("", 3, 3), c("", "Labour Force", "Population"))) %>%
  kable_styling(position="center")

5 (Potential) useful figures/maps

5.0.1 SSR

Don’t see immediate cause for concern? Perhaps some clustering in the southwest/appalachia but the counties themselves are also smaller in size so might optically trick?

glht(main, linfct = "mines_diff + lag_diff + lag_diff2 = 0") %>% summary

     Simultaneous Tests for General Linear Hypotheses

Fit: feols(fml = FE_diffuer, data = allcomp, se = "twoway")

Linear Hypotheses:
                                       Estimate Std. Error t value Pr(>|t|)
mines_diff + lag_diff + lag_diff2 == 0 -0.02443    0.02690  -0.908    0.364
(Adjusted p values reported -- single-step method)
glht(main_cc, linfct = "mines_diff + lag_diff + lag_diff2 = 0") %>% summary

     Simultaneous Tests for General Linear Hypotheses

Fit: feols(fml = FE_diffuer, data = allcomp_cc, se = "twoway")

Linear Hypotheses:
                                       Estimate Std. Error t value Pr(>|t|)
mines_diff + lag_diff + lag_diff2 == 0 -0.01171    0.02216  -0.528    0.597
(Adjusted p values reported -- single-step method)
ssr_df <- allcomp %>% slice(main$obs_selection$obsRemoved) %>% mutate(residuals = main$residuals) %>%
  group_by(fips) %>%
  summarise(ssr = sum(residuals^2))

allcompva <- df_va(allcomp)

ssr_df_va <- df_va(ssr_df)
plot_usmap(data = ssr_df_va, values = "ssr", regions = "counties", col = "black", size = 0.07, exclude = c("AK", "HI" )) +
  labs(title = "Geographical Distribution of SSR") + 
  scale_fill_viridis(name = "ssr",  na.value = "seashell2") +
  theme(panel.background = element_rect(colour = "black", fill = "gray90"))

hist(ssr_df$ssr)

5.0.2 UER

means_overall <- allcomp %>% 
   group_by(fips) %>%
   summarize(uer = mean(uer), REE_scaled = sum(REE_inv_scaled_realgdp))

means_overallva <- df_va(means_overall)

hist(allcompva$mines_diff[allcompva$mines_diff != 0])

summary(allcompva$mines_diff[allcompva$mines_diff != 0])
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-30.000  -2.000  -1.000  -0.468   1.000  20.000 
plot_usmap(data = filter(allcompva, year == 2019), values = "uer", regions = "counties", col = "black", size = 0.07, exclude = c("AK", "HI" )) +
   labs(title = "Geographical Distribution of Unemployment Rate in 2019") + 
   scale_fill_viridis(name = "uer", na.value = "seashell2", direction = -1, option = "magma") +
   theme(panel.background = element_rect(color = "black", fill = "gray90"), legend.position = "left")

5.0.3 UER over time

This would ideally be animated or grid arranged but have not yet figured it out :)

# Goal: animate or grid.arrange
uer_list = list()
for(i in 2002:2019){
   j = i - 2001
   p1 <- plot_usmap(data = filter(allcompva, year == i), values = "uer", regions = "counties", col = "black", size = 0.07, exclude = c("AK", "HI" )) +
     labs(title = paste0("Geographical Distribution of Unemployment Rate in ", i)) + 
     scale_fill_viridis(name = "uer",  na.value = "seashell2", direction = -1) +
     theme(panel.background = element_rect(color = "black", fill = "gray90"), legend.position = "left")
   print(p1)
   uer_list[[j]] <- p1
}

5.0.4 Other indicators over time

yearsums <- allcomp %>%
  group_by(year) %>%
  summarize(REE = sum(REE_inv), TAA = sum(total_taa), active_mines = sum(active_mines), 
            mines_diff = sum(mines_diff), uer = mean(uer), emp = sum(emp), 
            employed = sum(employed), unemployed = sum(unemployed), 
            labour_force = sum(labour_force), labor_hours = mean(labor_hours, na.rm = TRUE))

yearsums$uercalc <- (yearsums$unemployed/yearsums$labour_force)*100

reeplot <- ggplot(yearsums, aes(x=year, y=REE)) +
  geom_line()+
  geom_point()+
  labs(title="Renewable energy investments between 2002-2019",x="Year", y = "Renewable Energy Investments")+
  scale_color_viridis(option = "D") +
  scale_fill_viridis(option = "D")

taaplot <- ggplot(yearsums, aes(x=year, y=TAA)) +
  geom_line()+
  geom_point()+
  labs(title="TAA funding between 2002-2019",x="Year", y = "Renewable Energy Investments")

mines_diffplot <- ggplot(yearsums, aes(x=year, y=mines_diff)) +
  geom_line()+
  geom_line(linetype = "dashed", aes(y = 0))+
  geom_point()+
  labs(title="Change in active mines between 2002-2019",x="Year", y = "Change in active mines")

mines_diff_cty <- ggplot(allcomp, aes(x=year, y = mines_diff)) +
  geom_jitter(aes(color = state.x))+
  geom_point(aes(y = max(mines_diff)))+
  labs(title="Change in active mines between 2002-2019",x="Year", y = "Change in active mines")


emp_lfplot <- ggplot(yearsums, aes(x = year)) +
  geom_line(aes(y = employed, color = "Employed persons")) +
  geom_line(aes(y = labour_force, color = "Labour force"))+
  theme(legend.position = "bottom")+
  labs(title="Employed, Labour Force",x="Year")

unemp_plot <- ggplot(yearsums, aes(x = year, y = labour_force)) +
  geom_line(aes(y = labour_force, color = "Labour force"))+
  theme(legend.position = "bottom")+
  labs(title="Unemployed Persons",x="Year")

uerplot <-ggplot(yearsums, aes(x = year, y = uercalc)) +
  geom_line()+
  theme(legend.position = "bottom")+
  labs(title="National unemployment rate",x="Year")

mines_emp <- ggplot(yearsums, aes(x = year)) +
  geom_line(aes(y = active_mines, color = "Active Mines")) +
  geom_line(aes(y = emp/100, color = "Persons Employed in Coal")) +
  scale_y_continuous(
    name = "Active Mines",
    sec.axis = sec_axis(~.*100, name="Persons Employed in Coal (Hundreds)")
  ) +
  theme(legend.position = "bottom",
    legend.title = element_blank(),
    axis.title.y = element_text(size=13, face = "italic", hjust = 0.5),
    axis.title.y.right = element_text(size=13, face = "italic", hjust = 0.5),
    title = element_text(size = 12)
  ) +
  scale_color_viridis(discrete = TRUE, direction = -1)

grid.arrange(reeplot, taaplot, mines_diffplot, mines_emp, ncol=2, nrow =2)

grid.arrange(uerplot, emp_lfplot, unemp_plot, ncol=2, nrow =2)

6 Interactive Fixed Effects/Factor Structure Efforts

6.1

6.1.1 Testing for presence of unobserved common factors

Confirmed!

library(phtt)

# Create matrices of most often used variables
dep_diff_uer <- c_mat(allcomp$diff_uer)
ind_mines_diff <- c_mat(allcomp$mines_diff)
ind_lag_diff <- c_mat(allcomp$lag_diff)
ind_lag_diff2 <- c_mat(allcomp$lag_diff2)
# ind_closure <- c_mat(allcomp$mine_closure)
# ind_lag_closure <- c_mat(allcomp$lag_closure)
ind_difflogrealgdppc <- c_mat(allcomp$diff_log_realgdp_pc)
ind_difflogrealgdp <- c_mat(allcomp$diff_log_realgdp)
ind_difflogpop <- c_mat(allcomp$diff_log_pop)
dep_logemp <- c_mat(allcomp$diff_log_employed)
dep_logunemp <- c_mat(allcomp$diff_log_unemployed)
dep_loglf <- c_mat(allcomp$diff_log_lf)
dep_logpop <- c_mat(allcomp$diff_log_pop)

################################################################################
# Test for potential interactive FE (beyond additive two-ways fixed effects) as 
# outlined in R package documentation. Result: "Both models have time-varying 
# interactive effects"

# Optimal dimensions = 3 (?) judging by the plots below
fm = dep_diff_uer ~ -1 + ind_mines_diff + ind_lag_diff + ind_lag_diff2 + ind_difflogrealgdppc

test_eup <- Eup(dep_diff_uer ~ -1 + ind_mines_diff + ind_lag_diff + ind_lag_diff2 + ind_difflogrealgdppc , additive.effects = "twoways")
test_kss <- KSS(dep_diff_uer ~ -1 + ind_mines_diff + ind_lag_diff + ind_lag_diff2 + ind_difflogrealgdppc , additive.effects = "twoways")

# Both tests confirm existence of time-varying interactive effects
checkSpecif(test_eup, level = 0.001)
----------------------------------------------
Testing the Presence of Interactive Effects
Test of Kneip, Sickles, and Song (2012)
----------------------------------------------
H0: The factor dimension is equal to 0.

Test-Statistic        p-value    crit.-value     sig.-level 
        167.79           0.00           3.09           0.00 
checkSpecif(test_kss, level = 0.001)
----------------------------------------------
Testing the Presence of Interactive Effects
Test of Kneip, Sickles, and Song (2012)
----------------------------------------------
H0: The factor dimension is equal to 0.

Test-Statistic        p-value    crit.-value     sig.-level 
        487.80           0.00           3.09           0.00 
################################################################################
OptDim(test_eup, criteria = c("IPC1", "PC1", "PC2"), standardize = TRUE)
Call: OptDim.default(Obj = test_eup, criteria = c("IPC1", "PC1", "PC2"), 
    standardize = TRUE)

---------
Criteria of Bai and Ng (2002):

  PC1 PC2
    2   2

---------
Criterion of Bai (2004):

  IPC1
     0
plot(OptDim(test_kss, standardize = TRUE), main = NULL, xlab = "Proposed # of Factors")

#"Scree Plot: Optimal Factor Choices Reported by Various Factor/Principal Component Analysis Methods"))
# Removing LA counties removes 4-factor suggestion. Only 1 or 2

# kable(test_kss$optimal.dim)

6.1.2 Individual effects only

In preliminary application of phtt package with two-way fixed effects, the third forced factor seemed to follow the behavior of the time-fixed effect. Thus, individual fixed effects were attemped (instead of two-way) with 2,3,4 factors included. However, plotting estimated factors (see plots below) shows that the time-fixed effect ‘disappears’ (ie. is not accounted for) as the forced factors no longer take up the time-fixed effect behavior of the dependent variable. Thus, I assume that two-way fixed effects is still the way to go with this model. Below, included individual fixed effects models with 2,3,4 factors in case of interest.

# None of the factors replicate the time fixed effect...thus unlikely that "individual" is the way to go??
# KSS estimation with individual effects only and 2, 3, 4 factors
change1_KSS <- KSS(dep_diff_uer ~ -1 + ind_mines_diff + ind_lag_diff + ind_lag_diff2 + ind_difflogrealgdppc, additive.effects = "individual", factor.dim = 1)
change2_KSS <- KSS(dep_diff_uer ~ -1 + ind_mines_diff + ind_lag_diff + ind_lag_diff2 + ind_difflogrealgdppc, additive.effects = "individual", factor.dim = 2)

summary(change1_KSS)
Call:
KSS.default(formula = dep_diff_uer ~ -1 + ind_mines_diff + ind_lag_diff + 
    ind_lag_diff2 + ind_difflogrealgdppc, additive.effects = "individual", 
    factor.dim = 1)

Residuals:
   Min     1Q Median     3Q    Max 
 -8.48  -0.62  -0.16   0.33  12.83 


 Slope-Coefficients:
                     Estimate   StdErr z.value    Pr(>z)    
ind_mines_diff       -0.07650  0.01020   -7.53  5.27e-14 ***
ind_lag_diff          0.02460  0.00952    2.58   0.00978 ** 
ind_lag_diff2         0.06160  0.00922    6.68  2.32e-11 ***
ind_difflogrealgdppc -1.89000  0.06530  -28.90 < 2.2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Additive Effects Type:  individual  

Used Dimension of the Unobserved Factors: 1  

Residual standard error: 1.57 on 46059 degrees of freedom 
R-squared: 0.0927 
plot(summary(change1_KSS))

summary(change2_KSS)
Call:
KSS.default(formula = dep_diff_uer ~ -1 + ind_mines_diff + ind_lag_diff + 
    ind_lag_diff2 + ind_difflogrealgdppc, additive.effects = "individual", 
    factor.dim = 2)

Residuals:
   Min     1Q Median     3Q    Max 
 -8.48  -0.62  -0.16   0.33  12.83 


 Slope-Coefficients:
                     Estimate   StdErr z.value    Pr(>z)    
ind_mines_diff       -0.07650  0.01050   -7.27  3.64e-13 ***
ind_lag_diff          0.02460  0.00985    2.50    0.0126 *  
ind_lag_diff2         0.06160  0.00955    6.46  1.07e-10 ***
ind_difflogrealgdppc -1.89000  0.06760  -27.90 < 2.2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Additive Effects Type:  individual  

Used Dimension of the Unobserved Factors: 2  

Residual standard error: 1.69 on 42970 degrees of freedom 
R-squared: 0.0927 
plot(summary(change2_KSS))

KSS_ind1 <- dw_compat_kss(change1_KSS)
KSS_ind2 <- dw_compat_kss(change2_KSS)

ife_ind_models <- rbind(KSS_ind1[1:3,], KSS_ind2[1:3,])

6.1.3 Two-way effects

Employing factor structure models using two-way fixed effects yield the following results.

Results:

  1. Again, these models corroborate the magnitude and direction of regression coefficients in Models 1 and 1 CC.
# KSS estimation with individual effects only and 1,2 factors
change1_KSStw <- KSS(dep_diff_uer ~ -1 + ind_mines_diff + ind_lag_diff + ind_lag_diff2 + ind_difflogrealgdppc, additive.effects = "twoways", factor.dim = 1)
change2_KSStw <- KSS(dep_diff_uer ~ -1 + ind_mines_diff + ind_lag_diff + ind_lag_diff2 + ind_difflogrealgdppc, additive.effects = "twoways", factor.dim = 2)

summary(change2_KSStw)
Call:
KSS.default(formula = dep_diff_uer ~ -1 + ind_mines_diff + ind_lag_diff + 
    ind_lag_diff2 + ind_difflogrealgdppc, additive.effects = "twoways", 
    factor.dim = 2)

Residuals:
   Min     1Q Median     3Q    Max 
 -8.53  -0.79  -0.25   0.30  12.38 


 Slope-Coefficients:
                     Estimate   StdErr z.value    Pr(>z)    
ind_mines_diff       -0.06470  0.00670   -9.65 < 2.2e-16 ***
ind_lag_diff         -0.00697  0.00628   -1.11     0.267    
ind_lag_diff2         0.03720  0.00608    6.11  9.78e-10 ***
ind_difflogrealgdppc -0.94100  0.04340  -21.70 < 2.2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Additive Effects Type:  twoways  

Used Dimension of the Unobserved Factors: 2  

Residual standard error: 0.681 on 42953 degrees of freedom 
R-squared: 0.193 
# Results: Regressing UER on change in active mines (+lag) using KSS specification 
# with 2,3,4 factors
#fixInNamespace("summary.KSS", "phtt")
# # Return ee
# n = 55422
# # 
# test1 <- summary(change1_KSStw)
# (1 + log(test1$RSS) +log((2*pi)/n))*(-1*(n/2))
# test2 <- summary(change2_KSStw)
# (1 + log(test2$RSS) +log((2*pi)/n))*(-1*(n/2))

# SSR increase after nfactors = 1.
# To report: 1,2 dimensions

# tabletest <- cbind(tablefun(test1), select(tablefun(test2), c(3)))
# tabletest
# names(tabletest)[3] <- "Factor_1"
# names(tabletest)[4] <- "Factor_2"

# kable(tabletest, booktabs=TRUE, format = "latex") %>% 
#    add_header_above() %>% 
#    kable_styling(position="center")
KSS_tw1 <- dw_compat_kss(change1_KSStw)
KSS_tw2 <- dw_compat_kss(change2_KSStw)

summary(change2_KSStw)
Call:
KSS.default(formula = dep_diff_uer ~ -1 + ind_mines_diff + ind_lag_diff + 
    ind_lag_diff2 + ind_difflogrealgdppc, additive.effects = "twoways", 
    factor.dim = 2)

Residuals:
   Min     1Q Median     3Q    Max 
 -8.53  -0.79  -0.25   0.30  12.38 


 Slope-Coefficients:
                     Estimate   StdErr z.value    Pr(>z)    
ind_mines_diff       -0.06470  0.00670   -9.65 < 2.2e-16 ***
ind_lag_diff         -0.00697  0.00628   -1.11     0.267    
ind_lag_diff2         0.03720  0.00608    6.11  9.78e-10 ***
ind_difflogrealgdppc -0.94100  0.04340  -21.70 < 2.2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Additive Effects Type:  twoways  

Used Dimension of the Unobserved Factors: 2  

Residual standard error: 0.681 on 42953 degrees of freedom 
R-squared: 0.193 
ife_tw_models <- rbind(KSS_tw2[1:3,],KSS_tw1[1:3,])

ife_tw_models$term <- substr(ife_tw_models$term, 5, nchar(ife_tw_models$term))

6.1.4 IFE Application of Overall Economy Model

emp_KSS <- KSS(dep_logemp ~ -1 + ind_mines_diff + ind_lag_diff + ind_lag_diff2 + ind_difflogrealgdp + ind_difflogpop, additive.effects = "twoways", factor.dim = 1)
unemp_KSS <- KSS(dep_logunemp ~ -1 + ind_mines_diff + ind_lag_diff + ind_lag_diff2 + ind_difflogrealgdp + ind_difflogpop, additive.effects = "twoways", factor.dim = 1)
lf_KSS <- KSS(dep_loglf ~ -1 + ind_mines_diff + ind_lag_diff + ind_lag_diff2 + ind_difflogrealgdp + ind_difflogpop, additive.effects = "twoways", factor.dim = 1)
pop_KSS <- KSS(dep_logpop ~ -1 + ind_mines_diff + ind_lag_diff + ind_lag_diff2 + ind_difflogrealgdp, additive.effects = "twoways", factor.dim = 1)

mergedecon <- cbind(tablefun(summary(emp_KSS))[-2],
               tablefun(summary(unemp_KSS))[3],
                        tablefun(summary(lf_KSS))[3],
                                 rbind(tablefun(summary(pop_KSS)),NA,NA)[3])


kable(mergedecon, booktabs=TRUE, format = "latex") %>%
  add_header_above(c("Variables", "Employed Persons", "Unemployed Persons", "Labour Force", "Population")) %>%
  kable_styling(position="center")

6.1.5 (Ignore atm) PHTT: Replicate regressions incorporating REE interactions

Run on principal regressions with binary mine closure indicator incorporating USDA REE investment interaction.

Note: Interaction variables created above via static multiplication of REE binary closure, and lag of binary closure variables. This could potentially pose an issue with SE?

################################################################################
# Incorporating interaction between REE investments and whether there was a 
# mine closure in that year.

allcomp$intREE = allcomp$REE_bin_top90*allcomp$mines_diff
allcomp$intREElag = allcomp$REE_bin_top90*allcomp$lag_diff
allcomp$intREElag2 = allcomp$REE_bin_top90*allcomp$lag_diff2

test_REE_KSS <- KSS(dep_diff_uer ~  -1 + ind_mines_diff + ind_lag_diff + ind_lag_diff2 + c_mat(allcomp$REE_bin_top90) +
                    c_mat(allcomp$intREE) + c_mat(allcomp$intREElag) + c_mat(allcomp$intREElag2)+ 
                    ind_difflogrealgdppc, 
                    additive.effects = "twoways")

checkSpecif(test_REE_KSS, level = 0.001)
----------------------------------------------
Testing the Presence of Interactive Effects
Test of Kneip, Sickles, and Song (2012)
----------------------------------------------
H0: The factor dimension is equal to 0.

Test-Statistic        p-value    crit.-value     sig.-level 
        378.42           0.00           3.09           0.00 
# Rejects null of cross-sectional independence
# Test OptDim: reveals 1,2,4 factors
OptDim(test_REE_KSS, standardize = TRUE)
Call: OptDim.default(Obj = test_REE_KSS, standardize = TRUE)

---------
Criterion of Kneip, Sickles, and Song (2012):

  KSS.C
      0

---------
Criteria of Bai and Ng (2002):

  PC1 PC2 PC3 BIC3 IC1 IC2 IC3
    2   2   2    0   1   1   1

--------
Criteria of Ahn and Horenstein (2013):

  ER GR
   2  2

---------
Criteria of Bai (2004):

  IPC1 IPC2 IPC3
     0    0    0

---------
Criterion of Onatski (2009):

  ED
   0

---------
ABC calibration of IC1 and IC2 (Alessi et al. (2010)):

  ABC.IC1 ABC.IC2
        1       1
################################################################################


reebin1_KSS <- KSS(dep_diff_uer ~ -1 + ind_mines_diff + ind_lag_diff + ind_lag_diff2 + c_mat(allcomp$REE_bin_top90) +
                    c_mat(allcomp$intREE) + c_mat(allcomp$intREElag) + c_mat(allcomp$intREElag2)+ 
                    ind_difflogrealgdppc, 
                    additive.effects = "twoways", factor.dim = 1)

reebin2_KSS <- KSS(dep_diff_uer ~ -1 + ind_mines_diff + ind_lag_diff + ind_lag_diff2 + c_mat(allcomp$REE_bin_top90) +
                    c_mat(allcomp$intREE) + c_mat(allcomp$intREElag) + c_mat(allcomp$intREElag2)+ 
                    ind_difflogrealgdppc, 
                    additive.effects = "twoways", factor.dim = 2)

# Results: Regressing UER on mines_diff (+2 time lags) and each of these
# interacted with a binary of whether there was USDA-reported renewable energy investment using 
# KSS specification and 2,3,4 factors
# testree1 <- summary(reebin1_KSS)
# round(testree1$R2, 3)
# testree1$KSS.obj
# (1 + log(testree1$RSS) +log((2*pi)/n))*(-1*(n/2))
# testree2 <- summary(reebin2_KSS)
# (1 + log(testree2$RSS) +log((2*pi)/n))*(-1*(n/2))
# 
# tabletest <- cbind(tablefun(testree1), select(tablefun(testree2), c(3)))
# names(tabletest)[3] <- "Factor_1"
# names(tabletest)[4] <- "Factor_2"
# names(tabletest)
# 
# totaltbl <- rbind(tabletest, data.frame(var = NA,measure = NA, Factor_1 = c(round(testree1$R2, 3), "two-ways", 1),
# Factor_2 = c(round(testree2$R2, 3), "two-ways", 2)))
# testree1

# kable(totaltbl[-2], booktabs=TRUE, format = "latex") %>%
#   add_header_above() %>%
#   kable_styling(position="center")

7 Coefficient Plots

Results:

  1. Main results displayed in coefficient plots show that each model employed for the relationship between the change in active mines and change in unemployment rate estimate the same direction and magnitude of the regression coefficients on the change in active mines regressor (t = t and 1 or 2 time lags). Useful or non-interesting?

7.1

7.1.1 FEOLS (General)

# Coefficient plots of FEOLS
models <- list(
  "Total US Counties (FEOLS)" = feols(diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc | fips + year, allcomp, se = 'twoway'),
  "Coal Counties Only (FEOLS)" = feols(diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc | fips + year, allcomp_cc, se = 'twoway'))


modelplot(models, coef_map = cm, background = list(geom_vline(xintercept = 0, color = "grey")))+
   labs(x = 'Coefficients (Δ Unemployment Rate)', 
         title = 'Coefficient estimates of FEOLS models')+
    scale_color_manual(values =  viridis(n = 4))+
  theme(panel.background = element_rect("white"))

7.1.2 Spatial models

# Coefficient plots of spatial models

full <- tidy(feols(diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc | fips + year, allcomp, se = 'twoway'))
full$model <- "TWFE OLS (Total US)"
cc <- tidy(feols(diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc | fips + year, allcomp, se = 'twoway'))
cc$model <- "TWFE OLS (Coal Counties Only)"
full <- subset(full, term != "diff_log_realgdp_pc")
cc <- subset(cc, term != "diff_log_realgdp_pc")
full_cc <- rbind(subset(cc, term != "diff_log_realgdp_pc"), subset(full, term != "diff_log_realgdp_pc"))

allmodels <- rbind(spat_models, full_cc[c("estimate", "std.error","model","term")], ife_tw_models)
allmodelssave <- read_excel(here("data/coefplotdf.xlsx"))
dwplot(allmodels, vline = geom_vline(
           xintercept = 0,
           colour = "grey50"
       )) %>% relabel_predictors(
        c(
            mines_diff = "Δ Active Mines",
            lag_diff = "Δ Active Mines (t-1)",
            lag_diff2 = "Δ Active Mines (t-2)"
        )) +
  labs(x = 'Estimates and 95% Confidence Intervals'
       )+
  scale_color_manual(values =  viridis(n = 8), name = "Model Type")+
  theme(panel.background = element_rect(fill = "white"),
  panel.grid.major = element_line(size = 0.1, linetype = 'solid',
                                colour = "gray"))

7.1.3 IFE Factor Models

# Coefficient plots of IFE models with individual fixed effects only
# Likely incorrect as the additional factors do not mirror the TFE
dwplot(ife_ind_models, vline = geom_vline(
           xintercept = 0,
           colour = "grey60"
       )) %>% relabel_predictors(
        c(
            ind_mines_diff = "Δ Active Mines",
            ind_lag_diff = "Δ Active Mines (t-1)",
            ind_lag_diff2 = "Δ Active Mines (t-2)"
        )) +
  labs(x = 'Coefficients (Δ Unemployment Rate) and 95% Confidence Intervals', 
         title = "Coefficient estimates of IFE models")

# Coefficient plots of IFE models with two-way fixed effects
dwplot(ife_tw_models, vline = geom_vline(
           xintercept = 0,
           colour = "grey60"
       )) %>% relabel_predictors(
        c(
            ind_mines_diff = "Δ Active Mines",
            ind_lag_diff = "Δ Active Mines (t-1)",
            ind_lag_diff2 = "Δ Active Mines (t-2)"
        )) +
  labs(x = 'Coefficients (Δ Unemployment Rate) and 95% Confidence Intervals', 
         title = "Coefficient estimates of IFE models")

### FEOLS (By County Type)

# Coefficient plot of models on subset of Type 1, 2, and 3 counties
modelplot(model_types, coef_map = cm, background = list(geom_vline(xintercept = 0, color = "grey")))+
   labs(x = 'Coefficients (Δ Unemployment Rate)', 
         title = 'Coefficient estimates of FEOLS models') +
  scale_color_manual(values =  viridis(n = 3))

8 Summary stats

# Total REE (overall and in CC)
# $6,314,520,585 in overall US since 2002
# $122,238,781 in CC counties since 2002 (1.94%)

natlsums <- allcomp %>%
  summarize(REE = sum(REE_inv), TAA = sum(total_taa), mines_diff = sum(mines_diff))

# Total TAA (overall and in CC)
# $655,349,084,590 in overall US since 2009
# $65,952,811,622 in CC counties since 2009 (10.1%)
cc_sum <- allcomp_cc %>%
  summarize(REE = sum(REE_inv), TAA = sum(total_taa))

# Avg mines closed per yer
---
title: "Results to Date"
author: "Ebba Mark"
date: "`r Sys.Date()`"
output:
  html_document:
    theme: lumen
    code_download: yes
    toc: yes
    toc_float: yes
    toc_depth: 2
    number_sections: yes
    df_print: paged
  pdf_document:
    toc: yes
    toc_depth: '2'
---
  
```{r, echo=FALSE}
knitr::opts_chunk$set(comment = NA, echo = TRUE, eval = TRUE, 
                      warning = FALSE, message = FALSE)
```

```{r, include = FALSE}
rm(list = ls())
library(tidyverse)
library(rstatix)
library(here)
library(readxl)
library(plm)
library(conflicted)
library(fixest)
library(splm)
library(spdep)
library(zoo)
library(modelsummary)
library(dotwhisker)
library(sf)
library(raster)
library(dplyr)
library(spData)
library(tmap)
library(leaflet)
library(usmap)
library(ggplot2)
library(viridis)
library(gridExtra)
library(PDMIF)
library(multcomp)
library(stargazer)
library(texreg)
library(kableExtra)
library(pander)
library(beeswarm)
conflict_prefer("select","dplyr")
conflict_prefer("extract", "texreg")
conflict_prefer("filter", "dplyr")
conflict_prefer("here", "here")
conflict_prefer("lag", "dplyr")
conflict_prefer("select", "dplyr")

# Concerns to consider [X] if resolved:
# [X] 7 LA counties are missing observations from 2005-2007
# [X] VA Counties have been excluded from samples below due to inconsistency in 
#       FIPS format between BEA and Census 
# [ ] DC is removed from spatial matrix -- might be a useful proximity to consider (urban)
```


```{r datasets, include = FALSE}
# Dataset of relevant variables for analysis
allcomp <- read_excel(here("data/allcomp_final.xlsx"))

# Unmodified lookup table to adjust FIPS codes between various standards
bea_fips_mods <- read_excel(here("data/FIPSModificationsVA.xlsx"), skip = 1, col_types = c("text", "text","text","text"))

# Create subset of coal counties (CC) only (with active mines at some point)
cclist <- unique(allcomp$fips[which(allcomp$active_mines != 0)])
allcomp_cc <- subset(allcomp, fips %in% cclist)

# Dataset including the "type" of county as found in the Typology work (Typology Work_New.Rmd)
# Merged with CC dataset
cc_cluster <- read_excel(here("data/cc_clusters_251.xlsx"))
allcomp_cc_types <- merge(allcomp_cc, cc_cluster, by = "fips", all.x = TRUE)

# Load required functions and dictionaries
source("useful_functions.R")
source("dicts.R")
source("ref_lists.R")

```

```{r}
# Summary tables
# Variables labels are NOT assigned -- need to modify dictionaries above 
# when adding or removing variables from summary tables
allcompdf <- as.data.frame(allcomp)
allcompccdf <- as.data.frame(allcomp_cc)

stargazer(allcompdf[c("active_mines" ,"uer","employed", "unemployed", 
                      "labour_force", "pop", "REE_inv","production_shorttons",
                      "realgdp","realgdp_pc", "ruc", "ruc_bin","total_taa",
                      "REE_inv_scaled_realgdp")], title = "Summary Statistics: Contiguous US Counties", 
                      covariate.labels = sum_dict)

stargazer(allcompccdf[c("active_mines" ,"uer","employed", "unemployed", 
                        "labour_force", "pop", "REE_inv","production_shorttons",
                        "realgdp","realgdp_pc", "ruc", "ruc_bin","total_taa",
                        "REE_inv_scaled_realgdp")], title = "Summary Statistics: US Coal Counties",
                        covariate.labels = sum_dict, type = "text")

stargazer(allcompdf[c("diff_uer", "mines_diff","diff_log_realgdp", 
                      "diff_log_realgdp_pc", "diff_log_employed",
                      "diff_log_unemployed", "diff_log_lf", "diff_log_pop", 
                      "REE_bin_top90") ], 
                      title = "Summary Statistics of Transformed Variables: Contiguous US Counties",
                      covariate.labels = sum_dict_trans, type = "text")

stargazer(allcompccdf[c("diff_uer", "mines_diff","diff_log_realgdp", 
                        "diff_log_realgdp_pc", "diff_log_employed",
                        "diff_log_unemployed", "diff_log_lf", "diff_log_pop", 
                        "REE_bin_top90") ], 
                        title = "Summary Statistics of Transformed Variables: US Coal Counties Subset", 
                        covariate.labels = sum_dict_trans, type = "text")

```

```{r, stationarity tests, eval = FALSE, include = FALSE}
pdf <- pdata.frame(allcomp_full, index = c("fips", "year"))
pdfcc <- pdata.frame(allcomp_cc, index = c("fips", "year"))


relvars <- c("uer","diff_uer", "realgdp", "log_realgdp", "diff_log_realgdp", 
                                     "realgdp_pc", "logrealgdp_pc", "diff_log_realgdp_pc", 
                                     "employed", "log_employed", "diff_log_employed",
                                     "unemployed", "log_unemployed", "diff_log_unemployed",
                                     "labour_force", "log_lf", "diff_log_lf", 
                                     "pop", "log_pop", "diff_log_pop")
relvars_cc <-  c("active_mines", "mines_diff","REE_inv_scaled_realgdp","REE_bin_top90", "production_shorttons", "prod_diff")

stat_tests <- data.frame(var = relvars)
stat_tests_cc <- data.frame(var = relvars_cc)

rel_tot = data.frame()
for(l in 1:nrow(stat_tests)){
  lvar = stat_tests$var[[l]]
  print(lvar)
  pval1 = purtest(eval(parse(text = paste0("pdf$",lvar, sep = ""))), pmax = 4, test = "levinlin", exo = "none")
  pval2 = purtest(eval(parse(text = paste0("pdf$",lvar, sep = ""))), pmax = 4, test = "levinlin", exo = "intercept")
  pval3 = purtest(eval(parse(text = paste0("pdf$",lvar, sep = ""))), pmax = 4, test = "levinlin", exo = "trend")
  pval4 = purtest(eval(parse(text = paste0("pdf$",lvar, sep = ""))), pmax = 4, test = "hadri", exo = "trend")
  result1 = ifelse(pval1$statistic$p.value <= 0.001, pval1$statistic$alternative, paste0("NOT",pval1$statistic$alternative))
  result2 = ifelse(pval2$statistic$p.value <= 0.001, pval2$statistic$alternative, paste0("NOT",pval2$statistic$alternative))
  result3 = ifelse(pval3$statistic$p.value <= 0.001, pval3$statistic$alternative, paste0("NOT",pval3$statistic$alternative))
  rel_tot <- rbind(rel_tot,
                   c(lvar, pval1$statistic$method, pval1$statistic$p.value, result1),
                   c(lvar, pval2$statistic$method, pval2$statistic$p.value, result2),
                   c(lvar, pval3$statistic$method, pval3$statistic$p.value, result3))
}


 for(l in 1:nrow(stat_tests_cc)){
  lvar = stat_tests_cc$var[[l]]
  print(lvar)
  pvali = purtest(eval(parse(text = paste0("pdfcc$",lvar, sep = ""))), pmax = 4, exo = "intercept", test = "hadri")
  pvalt = purtest(eval(parse(text = paste0("pdfcc$",lvar, sep = ""))), pmax = 4, exo = "trend", test = "hadri")
  resulti = ifelse(pvali$statistic$p.value <= 0.001, pvali$statistic$alternative, paste0("NOT",pvali$statistic$alternative))
  resultt = ifelse(pvalt$statistic$p.value <= 0.001, pvalt$statistic$alternative, paste0("NOT",pvalt$statistic$alternative))
  res_veci = c(lvar, pvali$statistic$method, pvali$statistic$p.value, resulti)
  res_vect = c(lvar, pvalt$statistic$method, pvalt$statistic$p.value, resultt)
  rel_tot <- rbind(rel_tot, res_veci, res_vect)
 }

purtest(pdf$uer, pmax = 4, test = "madwu", exo = "intercept")
pval_int$statistic$alternative
pander(rel_tot)

rel_cips_tot = data.frame()
for(l in 1:nrow(stat_tests)){
  lvar = stat_tests$var[[l]]
  print(lvar)
  pval_int = cipstest(eval(parse(text = paste0("pdf$",lvar, sep = ""))), type = "trend")
  pval_trend = purtest(eval(parse(text = paste0("pdf$",lvar, sep = ""))), pmax = 4, test = "hadri", exo = "trend")
  result_int = ifelse(pval_int$statistic$p.value <= 0.001, pval_int$statistic$alternative, paste0("NOT",pval_int$statistic$alternative))
  result_trend = ifelse(pval_trend$statistic$p.value <= 0.001, pval_trend$statistic$alternative, paste0("NOT",pval_trend$statistic$alternative))
  res_vec = c(c(lvar, pval_int$statistic$method, pval_int$statistic$p.value, result_int),
              c(lvar, pval_trend$statistic$method, pval_trend$statistic$p.value, result_trend))
  rel_cips_tot <- rbind(rel_cips_tot, res_vec)
}

cipstest(pdf$uer, type = "none")
pvar(pdf)

```


```{r, include = FALSE}
# Create adjacency matrix for spatial model
##############################################################
# Import adjacency matrix from US Census Bureau: 
# https://www.census.gov/programs-surveys/geography/library/reference/county-adjacency-file.html
xmat1 <- read.csv("data/county_adjacency.txt", sep="\t", stringsAsFactors = FALSE, header = FALSE)

# Retain only columns with FIPS codes of each county (V2) and its neighbors (V4)
xmat1 <- xmat1[,c("V2","V4")]

# Replace NA values in V2 such that each row is a pair of neighbors
xmat1$V2 <- na.locf(xmat1$V2)

# Convert from integer to character fips code for appropriate matching
xmat1$V2 <- lapply(xmat1$V2, function(x) sapply(x, fips_format))
xmat1$V4 <- lapply(xmat1$V4, function(x) sapply(x, fips_format))

# Create lookup table to convert VA FIPS codes
getfips <- bea_fips_mods$`BEA FIPS`
names(getfips) <- bea_fips_mods$FIPS

xmat1[xmat1 == "46113"] <- "46102"
xmat1[xmat1 == "51515"] <- "51019"
xmat1$V2 <- lapply(xmat1$V2, function(x) ifelse(x %in% bea_fips_mods$FIPS, getfips[x], x))
xmat1$V4 <- lapply(xmat1$V4, function(x) ifelse(x %in% bea_fips_mods$FIPS, getfips[x], x))

# Removes Alaska, Hawaii, DC, PR, Guam, American Samoa
xmat1 <- subset(xmat1, substr(V2, 1, 2) != "60" & 
                  substr(V2, 1, 2) != "66" &
                  substr(V2, 1, 2) != "69" &
                  substr(V2, 1, 2) != "72" &
                  substr(V2, 1, 2) != "78" &
                  substr(V2, 1, 2) != "15" &
                  substr(V2, 1, 2) != "02")

LA_list <- c(LA_list_missing, "11001")
xmat1 <- subset(xmat1, !(V2 %in% LA_list) & !(V4 %in% LA_list))

length(unique(xmat1$V2))
#as.list(unique(xmat1$V2[which(!(xmat1$V2 %in% allcomp$fips))]))

# Unnest list elements for easier manipulation
xmat1 <- unnest(xmat1)
# Create a list of each unique FIPS code for matching
fipslist <- unique(xmat1$V2)

# Subset dataframe to only include FIPS present in the distance matrix
allcomp_nomissing <- subset(allcomp, fips %in% fipslist)
#allcomp_nomissing <- subset(allcomp_nomissing, fips != "51770")
allcomp_full <- allcomp_nomissing %>%
  arrange(fips) %>%
  select(fips,year,everything())

# # Spatial matrix includes counties that are not represented in dataset
# # Create list of fips in spatial matrix not in coal brief
# extraspat <- sapply(fipslist, function(x) !(x %in% allcomp$fips))
# extras <- fipslist[extraspat]

# Remove the fips in above list from spatial matrix to ensure fips match dataset perfectly
# # 51770 missing neighbors - remove for testing purposes ATM
# xmat <- subset(xmat1, !(V2 %in% extras) & !(V4 %in% extras) & V2 != "51770")

xmatfips <- unique(xmat1$V2)

# Create an empty (all cells = 0) n x n distance matrix with n = no of FIPS codes/counties
fmat <- matrix(data=0, nrow = length(xmatfips), ncol = length(xmatfips), dimnames = list(xmatfips,xmatfips))

# Iterate through xmatfips, populating matrix with 1 if two counties are neighbors
# Census bureau lists counties as neighbors of itself so the loop will fill cell 
# with 0 for row-pair that lists a county as a neighbor of itself
for(i in 1:nrow(xmat1)){
  a = as.character(xmat1$V2[i])
  b = as.character(xmat1$V4[i])
  if(identical(a,b)){fmat[a,b] <- 0}else{fmat[a,b] <- 1}
}

# Row standardises the distance matrix
fmat <- fmat/rowSums(fmat)
sum(is.na(fmat))
dim(fmat)

# Creates listw object for use in splm package
fmatlw <- mat2listw(fmat, style = "W")

# Also a test for errors
testfmatcdg <- listw2dgCMatrix(fmatlw)

# Sanity checks
### Ensure panel is balanced
is.pbalanced(allcomp_full)
### No NAs
sum(is.na(fmatlw))

```
# FIXEST regression results
*Overall Research Question: Do various indicators for changes in coal activity impact various county-level employment indicators?*

Employs standard two-way fixed effects panel regressions with county-clustered standard errors to a panel dataset of 3,072 contiguous counties observed between 2002-2019.

## Employment variables regressed on various measures of coal activity {.tabset}
### Change in active mines
**Results:**

1. Models 1: Significant change in unemployment rate in time t (in expected direction). Impact is near zero (albeit consistent sign as in t) in t+1 and changes sign in t+2 indicating a level effect as opposed to a growth effect. A one-unit decrease (increase) in active mines is associated with an increase (decrease) in the unemployment rate of 0.06 percentage points, negligible change in t+1 of the same direction, with a sign change and decrease(boost) of 0.03 percentage points. 1% significance level in time t. Indicates a non-growth effect with initial shock in time t (potentially t+1) but correcting again in t+2. (same conclusion for subset of coal counties).

2. Models 2/3: Change in employed/unemployed persons is also significant and in the expected direction. A one-unit increase in active mines is associated with a 0.12% increase in the total number of employed persons in time t (5% significance level) and 0.17% in time t+1 at the 1% significance level. (same conclusion for subset of coal counties)

3. Models 4: Change in labour force is only significant in t+1 indicating that individuals might leave the labour force following a mine closure. Though the result is very small... (same conclusion for subset of coal counties)

4. Models 5: Change in population is negligible (both in significance and magnitude). (same conclusion for subset of coal counties)

```{r level indicators}

# Level UER and level mines with various lags
lev22 <- feols(uer ~ l(active_mines, -2:0) + lag_mines + lag_mines2 + log_realgdp_pc | 
               fips + year, allcomp, panel.id = ~fips+year, se = 'twoway')
lev33 <- feols(uer ~ l(active_mines, -3:0) + lag_mines + lag_mines2 + lag_mines3 + log_realgdp_pc | 
               fips + year, allcomp, panel.id = ~fips+year, se = 'twoway')
lev03 <- feols(uer ~ active_mines + lag_mines +lag_mines2 + lag_mines3 + log_realgdp_pc | 
               fips + year, allcomp, se = 'twoway')
lev02 <- feols(uer ~ active_mines + lag_mines +lag_mines2 + log_realgdp_pc | 
               fips + year, allcomp, se = 'twoway')

# Level UER and mine levels on coal county subset
lev22cc <- feols(uer ~ l(active_mines, -2:0) + lag_mines + lag_mines2 + log_realgdp_pc | 
               fips + year, allcomp_cc, panel.id = ~fips+year, se = 'twoway')
lev33cc <- feols(uer ~ l(active_mines, -3:0) + lag_mines + lag_mines2 + lag_mines3 + log_realgdp_pc | 
               fips + year, allcomp_cc, panel.id = ~fips+year, se = 'twoway')
lev03cc <- feols(uer ~ active_mines + lag_mines +lag_mines2 + lag_mines3 + log_realgdp_pc | 
               fips + year, allcomp_cc, se = 'twoway')
lev02cc <- feols(uer ~ active_mines + lag_mines +lag_mines2 + log_realgdp_pc | 
               fips + year, allcomp_cc, se = 'twoway')
etable(lev03, lev22, lev33, lev03cc, lev22cc, lev33cc, order = c("active_mines,3", "active_mines,2", "active_mines,1", "Active Mines", "log_realgdp_pc"))

# Level mines and log emp indicators
lev_emplog <- feols(log_employed ~ active_mines + lag_mines + lag_mines2 + log_realgdp + log_pop | 
               fips + year, allcomp, se = 'twoway')
lev_unemplog <- feols(log_unemployed ~ active_mines + lag_mines + lag_mines2 + log_realgdp + log_pop | 
               fips + year, allcomp, se = 'twoway')
lev_lflog <- feols(log_lf ~ active_mines + lag_mines + lag_mines2 + log_realgdp + log_pop| 
               fips + year, allcomp, se = 'twoway')
lev_poplog <- feols(log_pop ~ active_mines + lag_mines + lag_mines2 + log_realgdp | 
               fips + year, allcomp, se = 'twoway')

etable(lev02, lev_emplog, lev_unemplog, lev_lflog, lev_poplog)

# Levels of everything
lev_emp <- feols(employed ~ active_mines + lag_mines + lag_mines2 + log_realgdp + log_pop | 
               fips + year, allcomp, se = 'twoway')
lev_unemp <- feols(unemployed ~ active_mines + lag_mines + lag_mines2 + log_realgdp + log_pop | 
               fips + year, allcomp, se = 'twoway')
lev_lf <- feols(labour_force ~ active_mines + lag_mines + lag_mines2 + log_realgdp + log_pop | 
               fips + year, allcomp, se = 'twoway')
lev_pop <- feols(pop ~ active_mines + lag_mines + lag_mines2 + log_realgdp | 
               fips + year, allcomp, se = 'twoway')
etable(lev_emp, lev_unemp, lev_lf, lev_pop)

# log dependent variables first-difference independent variables

uer_diff <- feols(uer ~ mines_diff + lag_diff + lag_diff2 + log_realgdp_pc | 
               fips + year, allcomp, se = 'twoway')
log_emp <- feols(log_employed ~ mines_diff + lag_diff + lag_diff2 + log_realgdp + log_pop | 
               fips + year, allcomp, se = 'twoway')

log_unemp <- feols(log_unemployed ~ mines_diff + lag_diff + lag_diff2 + log_realgdp + log_pop | 
               fips + year, allcomp, se = 'twoway')
log_lf <- feols(log_lf ~ mines_diff + lag_diff + lag_diff2 + log_realgdp + log_pop | 
               fips + year, allcomp, se = 'twoway')
log_pop <- feols(log_pop ~ mines_diff + lag_diff + lag_diff2 + log_realgdp | 
               fips + year, allcomp, se = 'twoway')
etable(uer_diff, log_emp, log_unemp, log_lf, log_pop)

```
# Regressions without combined time periods

The coefficient estimates are the same in each of these models isolating each variable.
```{r}

etable("t" = feols(diff_uer ~ mines_diff + diff_log_realgdp_pc | fips + year, allcomp, se = 'twoway'),
       "t-1" = feols(diff_uer ~ lag_diff + diff_log_realgdp_pc | fips + year, allcomp, se = 'twoway'),
       "t-2" = feols(diff_uer ~ lag_diff2 + diff_log_realgdp_pc | fips + year, allcomp, se = 'twoway'),
       "t-3" = feols(diff_uer ~ lag_diff3 + diff_log_realgdp_pc | fips + year, allcomp, se = 'twoway'),
       "t" = feols(diff_uer ~ mines_diff + diff_log_realgdp_pc | fips + year, allcomp_cc, se = 'twoway'),
       "t-1" = feols(diff_uer ~ lag_diff + diff_log_realgdp_pc | fips + year, allcomp_cc, se = 'twoway'),
       "t-2" = feols(diff_uer ~ lag_diff2 + diff_log_realgdp_pc | fips + year, allcomp_cc, se = 'twoway'),
       "t-3" = feols(diff_uer ~ lag_diff3 + diff_log_realgdp_pc | fips + year, allcomp_cc, se = 'twoway'),tex= TRUE,
       signifCode = c(`***` = 0.001, `**` = 0.01, `*` = 0.05, . = 0.1))

```


```{r}
# Regresses the change in unemployment rate, employed person, unemployed 
# persons, labour force, population on change in mines and two time lags using 
# full set of 3,079 contiguous US counties. 

FE_diffuer <- as.formula("diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc | fips + year")
FE_negdiffuer <- as.formula("neg_diffuer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc | fips + year")
FE_negdiffuer3 <- as.formula("neg_diffuer ~ mines_diff + lag_diff + lag_diff2 +lag_diff3 + diff_log_realgdp_pc | fips + year")

es <- feols(FE_negdiffuer, allcomp_full, panel.id = ~fips+year, se = 'twoway')
es_cc <- feols(FE_negdiffuer, allcomp_cc, panel.id = ~fips+year, se = 'twoway')
es3 <- feols(FE_negdiffuer3, allcomp_full, panel.id = ~fips+year, se = 'twoway')
es3_cc <- feols(FE_negdiffuer3, allcomp_cc, panel.id = ~fips+year, se = 'twoway')
es_2neg <- feols(neg_diffuer ~ l(mines_diff, -2:0) + lag_diff + lag_diff2 + diff_log_realgdp_pc | 
               fips + year, allcomp_full, panel.id = ~fips+year, se = 'twoway')
es_3neg <- feols(neg_diffuer ~ l(mines_diff, -3:0) + lag_diff + lag_diff2 + lag_diff3 + diff_log_realgdp_pc | 
               fips + year, allcomp_full, panel.id = ~fips+year, se = 'twoway')
es_2negcc <- feols(neg_diffuer ~ l(mines_diff, -2:0) + lag_diff + lag_diff2 + diff_log_realgdp_pc | 
               fips + year, allcomp_cc, panel.id = ~fips+year, se = 'twoway')
es_3negcc <- feols(neg_diffuer ~ l(mines_diff, -3:0) + lag_diff + lag_diff2 + lag_diff3 + diff_log_realgdp_pc | 
               fips + year, allcomp_cc, panel.id = ~fips+year, se = 'twoway')

etable(es, es_cc, es3, es3_cc, es_2neg, es_2negcc, es_3neg, es_3negcc)

fill_colors <- c(NA, "#39568CFF", "#95D840FF")
line_colors <- c(NA, "#440154FF", "#287D8EFF")
setFixest_coefplot(pt.join = TRUE, ci.join = FALSE, ci.fill = TRUE, ci.col = fill_colors, 
                   pt.col = line_colors, pt.pch = c(0, 19, 25), pt.bg = line_colors,
                   pt.cex = 1, ci.fill.par = list(col = fill_colors, alpha = 0.3), 
                   ci.lty = "dashed", ref.line = TRUE, ref.line.par = list(v = 4, col = "gray", lty = "solid"),
                   grid = TRUE, grid.par = list(vert = FALSE, lwd = 1), dict = plot_dict)

es_blank <- feols(diff_uer ~ l(mines_diff, -3:0) + lag_diff + lag_diff2 + lag_diff3 + diff_log_realgdp_pc | 
               fips + year, allcomp_cc, panel.id = ~fips+year, se = 'twoway')

coefplot(list(es_3negcc, es3, es3_cc), drop = c('diff_log_realgdp_pc'), xlim.add = c(0,0.001))
coefplot(list(es_blank, es_2neg, es_2negcc), drop = c('diff_log_realgdp_pc'), xlim.add = c(0,0.06))
coefplot(list(es_blank, es_3neg, es_3negcc), drop = c('diff_log_realgdp_pc'), xlab = "Years since negative change in active mines")

plot(NULL ,xaxt='n',yaxt='n',bty='n',ylab='',xlab='', xlim=0:1, ylim=0:1)
legend("topleft", legend = c("US Counties", "Coal Counties Only", "95% Confidence Interval", "95% Confidence Interval"), 
  col = c("#440154FF", "#287D8EFF", "#39568C4D", "#95D8404D"), 
  pt.bg = c("#440154FF", "#287D8EFF", NA, NA),
  pch = c(19,25,15,15),
  bty = "n", 
  pt.cex = c(1.5,1.5,3.5,3.5), 
  cex = 1, 
  text.col = "black", 
  ncol = 2,
  lty = c("solid", "solid", NA, NA),
  y.intersp = 1.5
  )
```

```{r, include = FALSE}

FE_diffuer3 <- as.formula("diff_uer ~ mines_diff + lag_diff + lag_diff2 +lag_diff3 + diff_log_realgdp_pc | fips + year")

es_ccpos <- feols(FE_diffuer, allcomp_cc, panel.id = ~fips+year, se = 'twoway')
es3pos <- feols(FE_diffuer3, allcomp, panel.id = ~fips+year, se = 'twoway')
es3_ccpos <- feols(FE_diffuer3, allcomp_cc, panel.id = ~fips+year, se = 'twoway')
es_2negpos <- feols(diff_uer ~ l(mines_diff, -2:0) + lag_diff + lag_diff2 + diff_log_realgdp_pc | 
               fips + year, allcomp, panel.id = ~fips+year, se = 'twoway')
es_3negpos <- feols(diff_uer ~ l(mines_diff, -3:0) + lag_diff + lag_diff2 + lag_diff3 + diff_log_realgdp_pc | 
               fips + year, allcomp, panel.id = ~fips+year, se = 'twoway')
es_2negccpos <- feols(diff_uer ~ l(mines_diff, -2:0) + lag_diff + lag_diff2 + diff_log_realgdp_pc | 
               fips + year, allcomp_cc, panel.id = ~fips+year, se = 'twoway')
es_3negccpos <- feols(diff_uer ~ l(mines_diff, -3:0) + lag_diff + lag_diff2 + lag_diff3 + diff_log_realgdp_pc | 
               fips + year, allcomp_cc, panel.id = ~fips+year, se = 'twoway')

etable(es3pos, es_2negpos, es_3negpos, es3_ccpos, es_2negccpos, es_3negccpos, 
       order = c("mines_diff,3", "mines_diff,2", "mines_diff,1", "Active Mines", "lag_diff3"))

```
### Change in active mines interacted with rural (1) vs. urban (0)
*Research Question: Are changes in the amount of active mines associated with changes in the unemployment rate, conditional on whether a county is rural or not? Ie. do changes in active mines impact rural and urban counties differently?*

Results:

1. Models 6: Including rural = 1 interaction term does not add much information. Likely that an already small unemployment rate change disaggregated by county type weakens ability to detect an impact?

```{r}
# First (second) model uses set of all US counties (subset of coal counties only).

etable("Model 6_i" = feols(diff_uer ~ mines_diff + lag_diff + lag_diff2 +  mines_diff:ruc_bin + lag_diff:ruc_bin + lag_diff2:ruc_bin + diff_log_realgdp_pc | 
               fips + year, allcomp, se = 'twoway'),
       "Model 6_i CC" = feols(diff_uer ~ mines_diff + lag_diff + lag_diff2 +  mines_diff:ruc_bin + lag_diff:ruc_bin + lag_diff2:ruc_bin + diff_log_realgdp_pc  | 
               fips + year, allcomp_cc, se = 'twoway'))

```

### Asymmetric treatment attempt
*Research Question: What is the impact of a positive (negative) change in active mines on county-level unemployment rate?*

The point estimates indicate a potential asymmetric treatment (mine closures lead to a larger change in unemployment rate as compared to the opening of a mine) effect but is not statistically significant.

Potential interesting result shows that the coefficient on change in mines is restricted to NEGATIVE changes. Essentially, this evidence shows that positive changes in active mines do not provide "windfall" employment, indicating that the solution is not to RETAIN or re-invigorate the mining sector.

```{r}
main <- feols(FE_diffuer, allcomp, se = 'twoway')
main_cc <- feols(FE_diffuer, allcomp_cc, se = 'twoway')
allcomp$pos_difflag <- ifelse(allcomp$lag_diff >= 0, 1, 0)
allcomp$pos_difflag2 <- ifelse(allcomp$lag_diff2 >= 0, 1, 0)
allcomp$neg_difflag <- ifelse(allcomp$lag_diff < 0, 1, 0)
allcomp$neg_difflag2 <- ifelse(allcomp$lag_diff2 < 0, 1, 0)

# Uses binary indicators for whether a change in active mines was positive (or zero) 
# versus negative.
# Below uses set of all contiguous US counties
model_7_neg= feols(diff_uer ~ mines_diff + lag_diff + lag_diff2 + neg_diff:mines_diff + 
                               neg_difflag:lag_diff + neg_difflag2:lag_diff2 + diff_log_realgdp_pc | 
                               fips + year, allcomp, se = 'twoway')

model_8_pos = feols(diff_uer ~ mines_diff + lag_diff +lag_diff2 + pos_diff:mines_diff + 
                               pos_difflag:lag_diff + pos_difflag2:lag_diff2 + diff_log_realgdp_pc | 
                            fips + year, allcomp, se = 'twoway')

model_9_both = feols(diff_uer ~ neg_diff:mines_diff + neg_difflag:lag_diff + neg_difflag2:lag_diff2 + pos_diff:mines_diff + pos_difflag:lag_diff + pos_difflag2:lag_diff2 + diff_log_realgdp_pc | fips + year, allcomp, se = 'twoway')

etable(model_7_neg, model_8_pos, model_9_both, signifCode = c(`***` = 0.001, `**` = 0.01, `*` = 0.05, . = 0.1))
etable(model_9_both)

# Mines_diff is different from zero (statistically significant at the .1% level) (-0.056%)
glht(main, linfct = "mines_diff  = 0") %>% summary
glht(main, linfct = "mines_diff + lag_diff + lag_diff2 = 0") %>% summary

#glht(main, linfct = mcp(tension = c("mines_diff  = 0", "mines_diff + lag_diff + lag_diff2 = 0")))

# Mines_diff + mines_diff:neg_diff is different from zero (statistically significant at the 1% level)
# Higher coefficient value than in broader model (-0.078%)
glht(model_7_neg, linfct = "mines_diff + mines_diff:neg_diff = 0") %>% summary
glht(model_7_neg, linfct = "mines_diff + mines_diff:neg_diff + lag_diff + lag_diff:neg_difflag + lag_diff2 + lag_diff2:neg_difflag2 = 0") %>% summary

# Mines_diff and pos_diff is different from zero although not statistically significant and at a much lower magnitude! (-0.021%)
glht(model_8_pos, linfct = "mines_diff + mines_diff:pos_diff = 0") %>% summary
glht(model_8_pos, linfct = "mines_diff + mines_diff:pos_diff + lag_diff + lag_diff:pos_difflag + lag_diff2 + lag_diff2:pos_difflag2 = 0") %>% summary
```


```{r, eval = FALSE, include = FALSE}
allcomp_cc$pos_difflag <- ifelse(allcomp_cc$lag_diff >= 0, 1, 0)
allcomp_cc$pos_difflag2 <- ifelse(allcomp_cc$lag_diff2 >= 0, 1, 0)
allcomp_cc$neg_difflag <- ifelse(allcomp_cc$lag_diff < 0, 1, 0)
allcomp_cc$neg_difflag2 <- ifelse(allcomp_cc$lag_diff2 < 0, 1, 0)

# Below uses subset of coal counties only.
model_7_neg_cc = feols(diff_uer ~ mines_diff + lag_diff + lag_diff2 + neg_diff:mines_diff + 
                               neg_difflag:lag_diff + neg_difflag2:lag_diff2 + diff_log_realgdp_pc | 
                         fips + year, allcomp_cc, se = 'twoway')
model_8_pos_cc = feols(diff_uer ~ mines_diff + lag_diff +lag_diff2 + pos_diff:mines_diff + 
                               pos_difflag:lag_diff + pos_difflag2:lag_diff2 + diff_log_realgdp_pc |
                         fips + year, allcomp_cc, se = 'twoway')
model_9_both_cc =feols(diff_uer ~ neg_diff:mines_diff + neg_diff:lag_diff + neg_diff:lag_diff2 +
                         pos_diff:mines_diff + pos_diff:lag_diff + pos_diff:lag_diff2 + 
                         diff_log_realgdp_pc | fips + year, allcomp_cc, se = 'twoway')

etable(model_7_neg_cc, model_8_pos_cc, model_9_both_cc)

# Mines_diff is different from zero (statistically significant at the .1% level) (-0.041%)
glht(main_cc, linfct = "mines_diff  = 0") %>% summary
# Mines_diff + mines_diff:neg_diff is different from zero (statistically significant at the 1% level)
# Higher coefficient value than in broader model (-0.061%)
glht(model_7_neg_cc, linfct = "mines_diff + mines_diff:neg_diff = 0") %>% summary
glht(model_7_neg_cc, linfct = "mines_diff + mines_diff:neg_diff + lag_diff + lag_diff:neg_difflag + lag_diff2 + lag_diff2:neg_difflag2 = 0") %>% summary
# Mines_diff and pos_diff is different from zero although not statistically significant and at a much lower magnitude! (-0.009%)
glht(model_8_pos_cc, linfct = "mines_diff + mines_diff:pos_diff = 0") %>% summary
glht(model_8_pos_cc, linfct = "mines_diff + mines_diff:pos_diff + lag_diff + lag_diff:pos_difflag + lag_diff2 + lag_diff2:pos_difflag2 = 0") %>% summary
```
### Incorporating county types
The typology work is not included in this set of results. The following regressions take the set of 251 coal counties (as defined above) and subset into three groups depending on whether they are Type 1, 2, or 3 counties (see summary table in paper draft).

Results:

1. Main result is from CC Rural Model. Shows that the only type of county with statistically significant impacts from changes in active mines is the Type 1 rural, low-income, smaller, lower percentage of college education, lower female LFPR, etc. The effect detected here follows the same behavior as that in Model 1 and Model 1 CC.

```{r, running Fixest regressions with county type}
#| fig.width: 9
#| fig.height: 9
main <- feols(FE_diffuer, allcomp, se = 'twoway')
main_cc <- feols(FE_diffuer, allcomp_cc, se = 'twoway')

##### Run regressions from previous section incorporating the type of county. ########
# The county types (1,2,3) are derived from the clustering typology exercise outlined in the paper draft.
# Types are as follows: (rural:1; medium:2, urban:3)

cc_urbandf <- allcomp_cc_types %>% subset(type == 1)
cc_mediumdf <- allcomp_cc_types %>% subset(type == 2)
cc_ruraldf <- allcomp_cc_types %>% subset(type == 3)

cc_rural = feols(FE_diffuer, cc_ruraldf, se = "twoway")
cc_medium = feols(FE_diffuer, cc_mediumdf, se = "twoway")
cc_urban = feols(FE_diffuer, cc_urbandf, se = "twoway")

rural_2neg <- feols(diff_uer ~ l(mines_diff, -1:0) + lag_diff + lag_diff2 + lag_diff3 + diff_log_realgdp_pc | 
               fips + year, cc_ruraldf, panel.id = ~fips+year, se = 'twoway')
medium_2neg <- feols(diff_uer ~ l(mines_diff, -1:0) + lag_diff + lag_diff2 + lag_diff3 + diff_log_realgdp_pc | 
               fips + year, cc_mediumdf, panel.id = ~fips+year, se = 'twoway')
urban_2neg <- feols(diff_uer ~ l(mines_diff, -1:0) + lag_diff + lag_diff2 + lag_diff3 +diff_log_realgdp_pc | 
               fips + year, cc_urbandf, panel.id = ~fips+year, se = 'twoway')


fill_colors <- c("#39568CFF", "#95D840FF", "#eb7134")
line_colors <- c("#440154FF", "#287D8EFF", "#eb7134")
setFixest_coefplot(pt.join = TRUE, pt.join.par = list(lwd = 2, lty = c("solid", "dashed", "dashed")), 
                   ci.join = TRUE, ci.join.par = list(lty = c("dashed", "blank", "blank"), lwd = 0.5, col = line_colors), ci.fill = TRUE, ci.col = fill_colors, 
                   pt.col = line_colors, pt.pch = c(25, 23, 19), pt.bg = line_colors,
                   pt.cex = 1, ci.fill.par = list(col = fill_colors, alpha = 0.1), ci.lwd = 0.5, 
                   ci.lty = "blank", ref.line = TRUE, ref.line.par = list(v = 2, col = "gray", lty = "solid"),
                   grid = TRUE, grid.par = list(vert = FALSE, lwd = 1), dict = plot_dict, sep = 0)

coefplot(list(rural_2neg, medium_2neg, urban_2neg),drop = c('diff_log_realgdp_pc'), xlab = "Years since negative change in active mines")
legend("topright", legend = c("Type 1", "Type 2", "Type 3"), col = c( "#eb7134","#287D8EFF","#440154FF"), pt.bg= c( "#eb7134","#287D8EFF","#440154FF"), pch = c(19, 23, 25), cex = 1, 
  text.col = "black", lty = c("dashed", "dashed", "solid"))

# coefplot(list(rural_2neg),drop = c('diff_log_realgdp_pc'))
# coefplot(list(medium_2neg),drop = c('diff_log_realgdp_pc'))
# coefplot(list(urban_2neg),drop = c('diff_log_realgdp_pc'))

etable("CC Urban" = cc_urban, "CC Medium" = cc_medium, "CC Rural" = cc_rural, urban_2neg, medium_2neg, rural_2neg, order = c("mines_diff,1", "Change Active Mines"))

 model_types <- list(
  "Rural Type 1 Counties" = cc_rural,
  "Medium Type 2 Counties" = cc_medium,
  "Urban Type 3 Counties" = cc_urban)

 
test <- allcomp_cc_types %>% 
  group_by(fips) %>% 
  summarize(m1 = length(unique(mines_diff)), m1mean = mean(mines_diff),
            m2 = length(unique(lag_diff)), m2mean = mean(lag_diff),
            m3 = length(unique(lag_diff2)), m3mean = mean(lag_diff2))

test2 <- subset(test, (m1 == 1 & m1mean == 0) | (m2 == 1 & m2mean == 0) | (m3 == 1 & m3mean == 0))
const_fips <- unique(test2$fips)
allcomp_pdmif <- subset(allcomp_cc_types, !(fips %in% const_fips))
filter(allcomp_cc_types, fips %in% const_fips) %>% 
  group_by(fips) %>% 
  summarize(mean = mean(active_mines), max = max(active_mines), min = min(active_mines))
allcomp_pdmif %>% 
  group_by(fips) %>% 
  summarize(mean = mean(active_mines), max = max(active_mines), min = min(active_mines))


etable("REE Model Rural" = feols(diff_uer ~ mines_diff + lag_diff + lag_diff2 + mines_diff:REE_bin_top90 +
                             lag_diff:REE_bin_top90 + lag_diff2:REE_bin_top90 + REE_bin_top90 + diff_log_realgdp_pc | fips + year, cc_ruraldf,  se = 'twoway'),
"REE Model Medium" = feols(diff_uer ~ mines_diff + lag_diff + lag_diff2 + mines_diff:REE_bin_top90 +
                             lag_diff:REE_bin_top90 + lag_diff2:REE_bin_top90 + REE_bin_top90 + diff_log_realgdp_pc | fips + year, cc_mediumdf,  se = 'twoway'),
            "REE Model Urban" = feols(diff_uer ~ mines_diff + lag_diff + lag_diff2 + mines_diff:REE_bin_top90 +
                             lag_diff:REE_bin_top90 + lag_diff2:REE_bin_top90 + REE_bin_top90 + diff_log_realgdp_pc | fips + year, cc_urbandf,  se = 'twoway'))
```


```{r, running Fixest regressions with county type PDMIF}
## Incorporating county types using PDMIF
# X can only have p columns where p = explanatory variables.

data4x <- select(allcomp_pdmif, c(mines_diff, lag_diff, lag_diff2, diff_log_realgdp_pc))
test <- as.matrix(data4x)
data4y <- select(allcomp_pdmif, c(year, fips, diff_uer))
yrs <- as.list(unique(data4y$year))
fps <- as.list(unique(data4y$fips))
data4y <- matrix(data = data4y$diff_uer, 18, 236, dimnames = list(yrs, fps))
groupmem <- allcomp_pdmif %>%
  group_by(fips) %>%
  summarize(type = unique(type))
groupmem <- data.frame(groupmem[,-1], row.names = groupmem$fips)

pdmif <- PDMIFLING(X = test, Y = data4y, Membership = groupmem, NGfactors = 1, NLfactors = c(2,2,2), Maxit = 30, tol = 0.01)

HYPTEST(pdmif$Coefficients,data.frame(c(0,1),c(-1,2)),pdmif$Se,"two",c(1,3),c(1,2))
dim(pdmif$Coefficients)
length(pdmif$GlobalFactors)
dim(pdmif$GlobalLoadings)
dim(pdmif$GroupFactors)
pdmif$GroupLoadings
pdmif$Coefficients
colnames(pdmif$Coefficients) <- colnames(data4y)

length(pdmif$Coefficients[1,which(groupmem$type == 3)])
length(pdmif$Coefficients[1,which(groupmem$type == 2)])
length(pdmif$Coefficients[1,which(groupmem$type == 1)])
pdmif$Coefficients[which(groupmem$type == 3)]

# pdmifshort <- pdmif$Coefficients[,colnames(pdmif$Coefficients)!="30003"]
# groupmemshort <- as.data.frame(groupmem[rownames(groupmem) != "30003",])
# names(groupmemshort) <- "type"

beeswarm(c(pdmif$Coefficients[1,],pdmif$Coefficients[2,],pdmif$Coefficients[3,]) ~ c(rep(1,236),rep(2,236), rep(3,236)),
          #col = c(rgb(0.25, 0.63, 1, 0.75), rgb(1, 0.88, 0.6, 0.75), rgb(0.97, 0.43, 0.37, 0.75)),
          pwcol = c(rep(groupmem$type), rep(groupmem$type),rep(groupmem$type)),
          pch=19, method="swarm", cex=0.5, corral="wrap", ylab="Value", xlab=NA, labels=c("mines_diff","lag_diff","lag_diff2"))

legend("topright", legend = c("1", "2", "3"),
       col = 1:3, pch = 19)

# allcomp %>% filter(fips == "30003") %>% ggplot(aes(y = uer, x = year))+
#   geom_line()+
#   geom_line(aes(y = mines_diff))


allcomp_cc_typespdmif <- subset(allcomp_cc_types, fips %in% allcomp_pdmif$fips)

urban_pdmif <- allcomp_cc_typespdmif %>% subset(type == 1)
medium_pdmif <- allcomp_cc_typespdmif %>% subset(type == 2)
rural_pdmif <- allcomp_cc_typespdmif %>% subset(type == 3)

cc_ruralpdmif = feols(FE_diffuer, rural_pdmif, se = "twoway")
cc_mediumpdmif = feols(FE_diffuer, medium_pdmif, se = "twoway")
cc_urbanpdmif = feols(FE_diffuer, urban_pdmif, se = "twoway")

etable(cc_rural, cc_ruralpdmif, cc_medium, cc_mediumpdmif, cc_urban, cc_urbanpdmif)
```

### Binary change in mines
Likely to exclude this estimation as results are not significant.
```{r}
# ----- on binary indicator for whether the change in mines is negative from the previous year
allcomp$lag_closure2[which(is.na(allcomp$lag_closure2))] <- 0
allcomp_cc$lag_closure2[which(is.na(allcomp_cc$lag_closure2))] <- 0
etable("Model 10" = feols(diff_uer ~ mine_closure + lag_closure + lag_closure2 + diff_log_realgdp_pc | fips + year, allcomp, se = 'twoway'), "Model 11" = feols(diff_uer ~ mine_closure + lag_closure + lag_closure2 + mine_closure:ruc_bin +
               lag_closure:ruc_bin + lag_closure2:ruc_bin + diff_log_realgdp_pc | fips + year, allcomp, se = 'twoway'), "Model 10 CC" = feols(diff_uer ~ mine_closure + lag_closure + lag_closure2 + diff_log_realgdp_pc | fips + year, allcomp_cc, se = 'twoway'),"Model 11 CC" = feols(diff_uer ~ mine_closure + lag_closure + lag_closure2 + mine_closure:ruc_bin +
               lag_closure:ruc_bin + lag_closure2:ruc_bin + diff_log_realgdp_pc | fips + year, allcomp_cc, se = 'twoway'))
```

### Change in coal production
Likely to exclude this estimation as results are not significant.
```{r, eval = FALSE, include = FALSE}
# ----- on change in county-level coal production in short tons
etable("Model 12" = feols(diff_uer ~ prod_diff + lag_prod_diff + diff_log_realgdp_pc | fips + year, allcomp, se = 'twoway'),
       "Model 12 CC" = feols(diff_uer ~ prod_diff + lag_prod_diff + diff_log_realgdp_pc | fips + year, allcomp_cc, se = 'twoway'),
       "Model 13" = feols(diff_employed ~ prod_diff + lag_prod_diff + log_realgdp + log_pop | fips + year, allcomp, se = 'twoway'),
       "Model 13 CC" = feols(diff_employed ~ prod_diff + lag_prod_diff + log_realgdp + log_pop | fips + year, allcomp_cc, se = 'twoway'))

# ----- on interaction between rural-urban code
etable("Model 14" = feols(diff_uer ~ prod_diff + lag_prod_diff + prod_diff:ruc_bin +
               lag_prod_diff:ruc_bin + diff_log_realgdp_pc | fips + year, allcomp, se = 'twoway'),
       "Model 14 CC" = feols(diff_uer ~ prod_diff + lag_prod_diff + prod_diff:ruc_bin +
               lag_prod_diff:ruc_bin + diff_log_realgdp_pc | fips + year, allcomp_cc, se = 'twoway'),
       "Model 15" = feols(diff_employed ~ prod_diff + lag_prod_diff + prod_diff:ruc_bin +
               lag_prod_diff:ruc_bin + log_realgdp + log_pop | fips + year, allcomp, se = 'twoway'),
       "Model 15 CC" = feols(diff_employed ~ prod_diff + lag_prod_diff + prod_diff:ruc_bin +
               lag_prod_diff:ruc_bin + log_realgdp + log_pop | fips + year, allcomp_cc, se = 'twoway'))
```


# Overall Economy Model
Coefficient interpretation....mines_diff impact on CHANGE in UER and CHANGE in LOG of emp, unemp, lf, and pop. Does this mean that the coefficient on mines_diff translates to "a one-unit increase in mines is correlated with a x% increase in the CHANGE in log of emp, unemp, lf and pop."

Isn't the question we want to answer the impact of mines_diff on the log of emp, unemp, lf, pop? Or are we comparing the change in the CHANGE in log of emp, etc compared to mean change?
```{r, overall economy model}
model_uer <- feols(FE_diffuer, allcomp, se = 'twoway')
model_emp <- feols(diff_log_employed ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp + diff_log_pop | fips + year, allcomp, se = 'twoway')
model_unemp <- feols(diff_log_unemployed ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp + diff_log_pop | fips + year, allcomp, se = 'twoway')
model_lf <- feols(diff_log_lf ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp + diff_log_pop | 
               fips + year, allcomp, se = 'twoway')
model_pop <- feols(diff_log_pop ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp | 
               fips + year, allcomp, se = 'twoway')

model_uercc <- feols(FE_diffuer, allcomp_cc, se = 'twoway')
model_empcc <- feols(diff_log_employed ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp + diff_log_pop | fips + year, allcomp_cc, se = 'twoway')
model_unempcc <- feols(diff_log_unemployed ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp + diff_log_pop | fips + year, allcomp_cc, se = 'twoway')
model_lfcc <- feols(diff_log_lf ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp + diff_log_pop | 
               fips + year, allcomp_cc, se = 'twoway')
model_popcc <- feols(diff_log_pop ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp | 
               fips + year, allcomp_cc, se = 'twoway')


etable("Model 1"= model_uer,
       "Model 2" = model_emp,
       "Model 3" = model_unemp,
       "Model 4" = model_lf,
       "Model 5" = model_pop)

# Performs same regressions on subset of coal counties only (defined as counties 
# that have had active mines between 2002-2019).
etable("Model 1 CC"= model_uercc,
       "Model 2 CC" = model_empcc,
       "Model 3 CC" = model_unempcc,
       "Model 4 CC" = model_lfcc,
       "Model 5 CC" = model_popcc)

model_list = list(model_uer, model_emp, model_unemp, model_lf, model_pop)
var_rows <-  c("Dependent Var.:", "Change Active Mines","Change Active Mines (t-1)","Change Active Mines (t-2)")

# econ_df <- data.frame(matrix(nrow = 4))
# for(k in 1:length(model_list)){
#   model = etable(model_list[k])
#   save_short <- subset(model, rownames(model) %in% var_rows)
#   econ_df <- cbind(econ_df, save_short)
# }
# econ_df
# test <- as.matrix(pvalue(model_emp)[1:3])

econ_df <- as.data.frame(rbind(create_cols(model_uer), create_cols(model_emp), create_cols(model_unemp), create_cols(model_lf), create_cols(model_pop)))
colnames(econ_df) <- c("t", "t-1", "t-2")
econ_df$model <- c("UER", "EMP", "UNEMP", "LF", "POP")
econ_dflong <- pivot_longer(econ_df, !model, names_to = "time", values_to = "fill")
#econ_dflong$fill <- as.factor(econ_dflong$fill)
econ_dflong$fill[which(econ_dflong$fill == 0)] <- NA
# writexl::write_xlsx(econ_dflong, here("data/"fulleconomydf.xlsx"))
ggplot(econ_dflong, aes(x = time, y = model, fill = fill))+
  scale_y_discrete(limits = rev(c("UER", "EMP", "UNEMP", "LF", "POP")), labels = rev(c("Model 1: \u0394 UER","Model 2: \u0394 (log) Employed persons", "Model 3: \u0394 (log) Unemployed persons", "Model 4: \u0394 (log) Labour force", "Model 5: \u0394 (log) Population")))+
  scale_x_discrete(limits = c("t", "t-1", "t-2"), labels = c("0","+1", "+2"))+
    geom_tile(color = "white",
            lwd = 1.5,
            linetype = 1)+
  scale_fill_gradientn(colours=c("navy","royalblue","lightskyblue","white", "mistyrose","red", "darkred"),limits = c(-4,4), na.value = "grey98", labels = c("(-)***","(-)**", "(+/-)*", "(+)**","(+)***"),labs(colour="Sign and Significance Level", size = 0.1))+
  ylab("Outcome Variable")+ 
  xlab("# years following mine closure")+
  theme(panel.background = element_blank(), panel.border = element_blank(), legend.title=element_text(size=8), )

```


## Effect of REE investments: {.tabset}
*Research Question: Do proximate renewable energy investments impact county-level employment impacts of mine closures?*

Employs standard two-way fixed effects panel regressions with county-clustered standard errors to a panel dataset of 3,072 contiguous counties observed between 2002-2019.
Interacts binary indicator for whether the total number of active mines in a county decreased from the previous year and whether REE investments exceeded 0.1% of county GDP.

Results:
1. No statistically significant impact of renewable energy investments detected.

```{r}
summary(allcomp$REE_inv_scaled_realgdp[allcomp$REE_inv_scaled_realgdp != 0])

#7728 observations of non-zero REE. 13.9% of observations have non-zero REE
# 460 observations have investment >= 1*county real GDP (8 also with active mines)
# 75 observations have investment >= 10*county real GDP
# 6 observations have investment >= 100*county real GDP
# 1st quartile upper limit is 0.0049* county real GDP

### 465 observations in coal subset with 162 with mines_diff != 0
test <- subset(allcomp, REE_inv_scaled_realgdp != 0)
quantile(test$REE_inv_scaled_realgdp, probs = seq(.1, .9))
summary(test$REE_inv_scaled_realgdp)
hist(test$REE_inv_scaled_realgdp[test$REE_inv_scaled_realgdp < 1], breaks = 100)

##### Preferable to use indicator for whether ANY REE as only 162 observations?
##### (of 7728 with REE investments) have non-zero mines_diff
## Went with top 10th percentile of data (7020 observations 152 with non-zero mines_diff)

etable("REE Model" = feols(diff_uer ~ mines_diff + lag_diff + lag_diff2 + mines_diff:REE_bin_top90 +
                             lag_diff:l(REE_bin_top90,1) + lag_diff2:l(REE_bin_top90,2) + REE_bin_top90 + diff_log_realgdp_pc | fips + year, allcomp, panel.id = ~fips+year,  se = 'twoway'),
       "REE Model CC" = feols(diff_uer ~ mines_diff + lag_diff + lag_diff2 + mines_diff:REE_bin_top90 +
                             lag_diff:REE_bin_top90 + lag_diff2:REE_bin_top90 + REE_bin_top90 + diff_log_realgdp_pc | fips + year, allcomp_cc,  se = 'twoway'), signifCode = c(`***` = 0.001, `**` = 0.01, `*` = 0.05, . = 0.1))

ree_model_main = feols(diff_uer ~ mines_diff + lag_diff + lag_diff2 + mines_diff:REE_bin_top90 +
                             lag_diff:REE_bin_top90 + lag_diff2:REE_bin_top90 + REE_bin_top90 + diff_log_realgdp_pc | fips + year, allcomp,  se = 'twoway')

etable(feols(diff_uer ~ mines_diff + mines_diff:REE_bin_top90 + mines_diff:l(REE_bin_top90, 1) + mines_diff:l(REE_bin_top90, 2) + diff_log_realgdp_pc | fips + year, allcomp, panel.id = ~fips+year,se = 'twoway'),
       feols(diff_uer ~ mines_diff + mines_diff:REE_bin_top90 + diff_log_realgdp_pc | fips + year, allcomp, panel.id = ~fips+year,se = 'twoway'),
       feols(diff_uer ~ mines_diff + mines_diff:l(REE_bin_top90, 1) + diff_log_realgdp_pc | fips + year, allcomp, panel.id = ~fips+year,se = 'twoway'),
       feols(diff_uer ~ mines_diff + mines_diff:l(REE_bin_top90, 2) + diff_log_realgdp_pc | fips + year, allcomp, panel.id = ~fips+year,se = 'twoway'))

etable(feols(diff_uer ~ lag_diff2 + diff_log_realgdp_pc | fips + year, allcomp, panel.id = ~fips+year,se = 'twoway'),
       feols(diff_uer ~ lag_diff2 + lag_diff2:l(REE_bin_top90, 1) + diff_log_realgdp_pc | fips + year, allcomp, panel.id = ~fips+year,se = 'twoway'),
       feols(diff_uer ~ lag_diff2 + lag_diff2:l(REE_bin_top90, 2) + diff_log_realgdp_pc | fips + year, allcomp, panel.id = ~fips+year,se = 'twoway'),
       feols(diff_uer ~ lag_diff2 + lag_diff2:l(REE_bin_top90, 2) + lag_diff2:l(REE_bin_top90, 1) + diff_log_realgdp_pc | fips + year, allcomp, panel.id = ~fips+year,se = 'twoway'), signifCode = c(`***` = 0.001, `**` = 0.01, `*` = 0.05, . = 0.1))
```


```{r, eval = FALSE, include = FALSE}
### Include specification with state-level renewable energy investment data
allcomp$REE_state_bin <- ifelse(allcomp$REE_inv_state != 0, 1, 0)
allcomp_cc$REE_state_bin <- ifelse(allcomp_cc$REE_inv_state != 0, 1, 0)

etable("REE Model" = feols(diff_uer ~ mines_diff + lag_diff + lag_diff2 + mines_diff:REE_bin_top90 +
                             REE_bin_top90 + mines_diff:REE_state_bin + diff_log_realgdp_pc | fips + year, allcomp,  se = 'twoway'),
       "REE Model CC" = feols(diff_uer ~ mines_diff + lag_diff + lag_diff2 + REE_state_bin + diff_log_realgdp_pc | fips + year, allcomp_cc,  se = 'twoway'))

glht(ree_model_main, linfct = "mines_diff + lag_diff + lag_diff2 = 0") %>% summary
glht(ree_model_main, linfct = "mines_diff + lag_diff + lag_diff2 + mines_diff:REE_bin_top90 + lag_diff:REE_bin_top90 + lag_diff2:REE_bin_top90 = 0") %>% summary
```

# Spatial models
## Spatial models {.tabset}
### Tests for cross-sectional dependence 
Cross-sectional dependence detected. Moran's I test and lagrange multiplier tests remain to be run!

```{r, eval = FALSE}
##############################################################
# library(lmtest)
# mainplm <- plm(diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc, allcomp_full, effect="twoways", index = c("fips","year"))

# Confirms fixed effects is the way to go
phtest(diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc, 
        data = allcomp_full, effect = "twoways", index = c("fips", "year"),
        test = c("cd", "sclm", "bcsclm", "lm", "rho", "absrho"),
        w = fmat)

# Confirms presence of cross sectional dependence
tests <- c("cd", "lm", "sclm", "rho", "absrho")

cdtest <- lapply(tests,function(x) pcdtest(diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc, 
        data = allcomp_full, effect = "twoways", index = c("fips", "year"),
        test = x,
        w = fmat))

pcdtest(diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc, 
        data = allcomp_full, effect = "twoways", index = c("fips", "year"),
        test = "rho",
        w = fmat)

# Randomization-based test of spatial dependence for panel models
# All of the following confirm cross-sectional dependence robust to global 
# dependence by common factors and persistence (serial correlation) in the data (Millo 2017)
#"rho" for the average correlation coefficient
# These return identical results...why?

rwtest(diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc, 
        data = allcomp_full, test = "rho", w = fmat, effect = "twoways", index = c("fips", "year"))
# "cd" for Pesaran's CD statistic
rwtest(diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc, 
        data = allcomp_full, test = "cd", w = fmat, effect = "twoways", index = c("fips", "year"))

# "sclm" for the scaled version of Breusch and Pagan's LM statistic,
rwtest(diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc, 
        data = allcomp_full, test = "sclm", w = fmat, effect = "twoways", index = c("fips", "year"))


# Below not required
# 
# sphtest(diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc, data = allcomp_full, effect = "twoways", listw = fmatlw, spatial.model = "error", method = "ML")
# sphtest(diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc, data = allcomp_full, effect = "twoways", listw = fmatlw, spatial.model = "lag", method = "ML")
# sphtest(diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc, data = allcomp_full, effect = "twoways", listw = fmatlw,spatial.model = "sarar",method = "ML")


# MORAN'S I TEST
#moran.lm<-lm.morantest(allcomp_full$diff_uer, fmatlw, alternative="two.sided")


# Vetor memory exhausted
# LAGRANGE MULTIPLIER TEST
# Locally robust LM tests for spatial lag (error) correlation sub spatial error (lag) correlation in panel models
#slmtest(diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc,
#        data = allcomp_full, listw = fmatlw, test="lme", model="within")
#slmtest(diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc,
#        data = allcomp_full, listw = fmatlw, test="lml", model="within")
#slmtest(diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc,
#        data = allcomp_full, listw = fmatlw, test="rlme", model="within")
#slmtest(diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc,
#        data = allcomp_full, listw = fmatlw, test="rlml", model="within")



```

```{r, cache = TRUE}
# This does not include 7 LA counties for which no employment indicators 
# were recorded in 2005 and 2006. Therefore, use allcomp_full as created 
# in the spatial matrix initiation section.

# Broomfield County, Colorado was only created in 2001 so no observation 
# exists for 2001 - will replace with 0 for completeness. It is only the 
# first observation...

allcomp_full$diff_log_realgdp_pc[which(allcomp_full$fips == "08014" & allcomp_full$year == 2002)] <- 0
allcomp_full$diff_log_realgdp[which(allcomp_full$fips == "08014" & allcomp_full$year == 2002)] <- 0
allcomp_full$diff_log_pop[which(allcomp_full$fips == "08014" & allcomp_full$year == 2002)] <- 0

fmdiff_uer <- diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc

# Required to extract Log Likelihood of error and SARAR model
#fixInNamespace("spsararlm", "splm")
#fixInNamespace("sperrorlm", "splm")
# (NOT DONE AS SPLAGLM RETURNS A MEASURE FOR LL) 
#fixInNamespace("splaglm", "splm")
# This third function (which returns a value for loglikelihood, transforms it according to:
# ens <- as.numeric((time * ldet  - ((n*time/2) * log(2 * pi)) - (n*time/2) * log(s2) - 1/(2 * s2) * SSE)) 
# and then returns ens instead of LL.......

#And manually add "ll=LL" as an element to the "return" list at the end of
#the function so that it reads:
#return <- list(coeff = betas, lambda = lambda, s2 = s2, rest.se = rest.se, 
#       lambda.se = lambda.se, asyvar1 = asyvar1, ll=LL)

sp_err_mines <- spml(fmdiff_uer, allcomp_full, 
                     index = NULL,  listw = fmatlw, lag = FALSE, na.action = na.fail, 
                     spatial.error = "b",  model = "within", effect = "twoways", quiet = FALSE)


sp_lag_mines <- spml(fmdiff_uer, allcomp_full, index = NULL,  listw = fmatlw,
                     lag = TRUE, na.action = na.fail,
                     spatial.error = "none",  model = "within", effect = "twoways", quiet = FALSE)


# Only used to test whether unadjusted loglikelihood yields different AIC and BIC results
# sp_lag_mines_unadjll <- spml(fmdiff_uer, allcomp_full, index = NULL,  listw = fmatlw,
#                      lag = TRUE, na.action = na.fail,
#                      spatial.error = "none",  model = "within", effect = "twoways", quiet = FALSE)

sp_err_lag_mines <- spml(fmdiff_uer, data = allcomp_full, index = NULL,  listw = fmatlw, 
                         lag = TRUE, na.action = na.fail, spatial.error = "b",
                         model = "within", effect = "twoways", quiet = FALSE)


# sp_err_mines$logLik
# sp_lag_mines$logLik
# sp_lag_mines_unadjll$logLik
# sp_err_lag_mines$logLik

# summary(sp_err_mines)
# summary(sp_lag_mines)
# summary(sp_lag_mines_unadjll)
# summary(sp_err_lag_mines)

sparse.W <- listw2dgCMatrix(fmatlw)
time <- length(unique(allcomp_full$year))
s.lwcounties <- kronecker(Matrix::Diagonal(time), sparse.W)
trMatc <- spatialreg::trW(s.lwcounties, type="mult")
implag <- spatialreg::impacts(sp_lag_mines, tr = trMatc, R = 200)
imperrlag <- spatialreg::impacts(sp_err_lag_mines, tr = trMatc, R = 200)

summary(sp_err_mines)
summary(implag, zstats=TRUE, short=TRUE)
summary(imperrlag, zstats=TRUE, short=TRUE)

mainfull <- feols(FE_diffuer, allcomp_full, se = 'twoway')
merged <- allcomp_full %>%
   mutate(residuals_lag = residuals(sp_lag_mines), residuals_lagerr = residuals(sp_err_lag_mines), residuals_err = residuals(sp_err_mines), residuals_main = residuals(mainfull))

#moran.mc(merged$residuals_lag, listw = fmatlw, 1000, zero.policy = TRUE)

for(i in 2002:2019){
  print(moran.mc(merged$residuals_lag[which(merged$year == i)], listw = fmatlw, 1000, zero.policy = TRUE))
}

mergedp <- pdata.frame(merged, index = c("fips", "year"))
#moran.mc(mergedp$residuals_lag, listw = fmatlw, 1000, zero.policy = TRUE)
# PCD cross-sectional dependence of residuals: 
# Main: reject independence: p-value: 2.2e-16
# rho: 0.0047673
pcdtest(mergedp$residuals_main, test = "cd")
pcdtest(mergedp$residuals_main, test = "rho")
# SAR: reject independence: p-value: 0.01644
# rho: -0.00026
pcdtest(mergedp$residuals_err,listw = fmatlw, test = "cd")
pcdtest(mergedp$residuals_err,listw = fmatlw, test = "rho")
# SLM: reject independence: p-value: 0.036
# rho: -0.00027
pcdtest(mergedp$residuals_lag, listw = fmatlw,test = "cd")
pcdtest(mergedp$residuals_lag, listw = fmatlw,test = "rho")

# SARAR: reject independence: p-value: 0.0425
# rho: -0.00022
pcdtest(mergedp$residuals_lagerr, listw = fmatlw, test = "cd")
pcdtest(mergedp$residuals_lagerr, listw = fmatlw, test = "rho")

ssr_spatial <- merged %>% 
  group_by(fips) %>%
  summarise(ssr_err = sum(residuals_err^2), ssr_lag = sum(residuals_lag^2), ssr_errlag = sum(residuals_lagerr^2), ssr_main = sum(residuals_main^2))

moran.mc(ssr_spatial$ssr_main, listw = fmatlw, 1000, zero.policy = TRUE)
moran.mc(ssr_spatial$ssr_err, listw = fmatlw, 1000, zero.policy = TRUE)
moran.mc(ssr_spatial$ssr_lag, listw = fmatlw, 1000, zero.policy = TRUE)
moran.mc(ssr_spatial$ssr_errlag, listw = fmatlw, 1000, zero.policy = TRUE)

ssr_spatial <- df_va(ssr_spatial)

for (i in 1:length(names(ssr_spatial)[-1])){
  mod = names(ssr_spatial[i+1])
  k <- plot_usmap(data = ssr_spatial, values = mod, regions = "counties", col = "black", size = 0.07, exclude = c("AK", "HI" )) +
  labs(title = paste0("Geographical Distribution of SSR in ",mod,"model")) +
  scale_fill_viridis(name = "ssr",  na.value = "seashell2") +
  theme(panel.background = element_rect(colour = "black", fill = "gray90"))
  print(k)
}

```


```{r, eval = FALSE, include = FALSE }
# Spatial model summary results
sums <- summary(implag, zstats=TRUE, short=TRUE)
#sums <- summary(imperrlag, zstats=TRUE, short=TRUE)

var_names = c("mines_diff", "lag_diff", "lag_diff2", "diff_log_realgdp_pc")
var_names_ = c("mines_diff", "lag_diff_", "lag_diff2", "diff_log_realgdp_pc")

tab = bind_rows(sums$res) %>% t %>% as.data.frame %>% setNames(., var_names) %>% mutate(Coef_Type=str_to_title(rownames(.)),
         Value_Type="Estimate") %>% bind_rows(sums$semat %>% t %>% as.data.frame %>% 
              mutate(Coef_Type=rownames(.),
                     Value_Type="Simulated SE")) %>% 
  bind_rows(sums$pzmat %>% t %>% as.data.frame %>% 
              mutate(Coef_Type=rownames(.),
                     Value_Type="p_values")) %>% 
   gather(key, value, "mines_diff", "lag_diff", "lag_diff2", "diff_log_realgdp_pc") 

tab1 = tab %>% 
  unite(coef, key, Value_Type) %>% 
  spread(coef, value) %>% 
  select(1,unlist(lapply(var_names_, function(x) grep(x, names(.))))) %>% 
  mutate_if(is.numeric, round, 3)

# Change column names to what we want to see in the output table
names(tab1) = c("", gsub(".*_(.*)", "\\1", names(tab1)[-1]))

# Output latex table, including extra header row to mark coefficient names
# kable(tab1, booktabs=TRUE, format = "latex") %>% 
#   add_header_above(setNames(c("", 3, 3, 3, 3), c("", "Active Mines", "Active Mines (t-1)", "Active Mines (t-2)", "(log) Real GDPPC"))) %>% 
#   kable_styling(position="center")

# Spatial error model output table
sum_sperr <- summary(sp_err_mines) %>% .$CoefTable %>% as.data.frame
names(sum_sperr)[4] <- "p_values"

sperr1 = sum_sperr %>% 
  mutate_at("p_values", function(x) ifelse(x <= 0.001, "***",
                                           ifelse(x > 0.001 & x <= 0.01, "**", 
                                                  ifelse(x > 0.01 & x <= 0.05, "*", 
                                                         ifelse(x > 0.05 & x <= 0.1, ".",""))))) %>%
  mutate_if(is.numeric, round, 3) %>% 
  mutate(Estimate = paste0(Estimate, p_values), var = c("Spatial error parameter", "Active Mines", "Active Mines (t-1)", "Active Mines (t-2)", "(log) Real GDPPC"), se = paste0("(",`Std. Error`,")")) %>%
  select(1,5,6) %>% 
  pivot_longer(!var, names_to = "measure", values_to = "estimate")

#, c("Rho", "Active Mines", "Active Mines (t-1)", "Active Mines (t-2)", "(log) Real GDPPC"))
# kable(sperr1[-2], booktabs=TRUE) %>% 
#   kable_styling(position="center")

```

```{r table1, eval = FALSE, include = FALSE}
# Reshape table into desired output format
tab1 = tab %>% 
  unite(coef, key, Value_Type) %>% 
  spread(coef, value) %>% 
  mutate_if(is.numeric, round, 3)

# Change column names to what we want to see in the output table
names(tab1) = c("", gsub(".*_(.*)", "\\1", names(tab1)[-1]))

# Output latex table, including extra header row to mark coefficient names
# kable(tab1, booktabs=TRUE, format="latex") %>% 
#   add_header_above(setNames(c("", 2, 2), c("", sort(rownames(sums$pzmat))))) %>% 
#   kable_styling(position="center")
```


```{r table2, eval = FALSE, include = FALSE}
# # Linear hypothesis testing
# library(car)
# # Can reject the null hypothesis that mines_diff, mines_diff + lag_diff is different from zero
# linearHypothesis(sp_err_mines, "mines_diff + lag_diff  = 0")
# linearHypothesis(sp_err_mines, "mines_diff + lag_diff +lag_diff2 = 0")
# 
# # CANNOT confidently reject that lag_diff2 does not return to the prior unemployment rate prior to the mine closure.
# linearHypothesis(sp_err_mines, "mines_diff + lag_diff +lag_diff2 = 0")

```

```{r, eval = FALSE, include = FALSE}

sptest = summary(sp_err_mines)
sptest$effects

# MuMIn::AICc(sp_err_mines)
# MuMIn::AICc(sp_lag_mines)
# MuMIn::AICc(sp_err_lag_mines)
# MuMIn::AICc(sp_lag_mines_unadjll)

#logLik, AIC and BIC
# Indicates that the order of preference of the models is as follows: lag, error-lag, lag(unadj), error
sp_lag_mines$logLik
sp_err_lag_mines$logLik
sp_err_mines$logLik
sp_lag_mines_unadjll$logLik

# class(MuMIn::AICc(sp_err_mines))
# sort(c(MuMIn::AICc(sp_err_mines), MuMIn::AICc(sp_lag_mines), MuMIn::AICc(sp_err_lag_mines), MuMIn::AICc(sp_lag_mines_unadjll)))

sp_list <-list(sp_err_mines, sp_lag_mines_unadjll, sp_err_lag_mines)
names(sp_list) = c("sp_error", "sp_lag_unadj", "sp_err_lag")

aic_bic <- cbind(as.data.frame(sapply(sp_list, function(x) x$logLik)), as.data.frame(sapply(sp_list, function(x) AICsplm(x ,criterion = c("AIC")))),
as.data.frame(sapply(sp_list, function(x) AICsplm(x ,criterion = c("BIC"))))) %>% arrange(.[,2])
colnames(aic_bic) <- c(" Log Likelihood", "AIC", "BIC")
rownames(aic_bic) <- c("Spatial Lag-Error Model", "Spatial Lag Model", "Spatial Error Model")
# 
# kable(aic_bic, format = "latex")

```

```{r, cache = TRUE}


sp_err <- dw_compat(sp_err_mines)
sp_lag <- dw_compat_imp(implag)
sp_lag$model <- "SLM"
sp_errlag <- dw_compat_imp(imperrlag)
sp_errlag$model <- "SARAR"
spat_models <- rbind(sp_err[2:4,], sp_lag[1:3,], sp_errlag[1:3,])
  
# Spatial Durbin model here!
# library(spatialreg)
# lagsarlm(fmdiff_uer, allcomp_LA, listw = fmatlw, na.action = na.fail, Durbin = TRUE)
```

### Employed overall 
```{r, cache = TRUE, eval = FALSE}

fmdiff_employed <- diff_log_employed ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp + diff_log_pop

emp_spat <- spml(fmdiff_employed, data = allcomp_full, index = NULL,  listw = fmatlw, 
                         lag = TRUE, na.action = na.fail, spatial.error = "b",
                         model = "within", effect = "twoways", quiet = FALSE)


impemp_spat <- spatialreg::impacts(emp_spat, tr = trMatc, R = 200)
summary(impemp_spat, zstats=TRUE, short=TRUE)


```

### Unemployed spatial
```{r, cache = TRUE, eval = FALSE}
fmdiff_unemp <- diff_log_unemployed ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp + diff_log_pop

unemp_spat <- spml(fmdiff_unemp, data = allcomp_full, index = NULL,  listw = fmatlw, 
                         lag = TRUE, na.action = na.fail, spatial.error = "b",
                         model = "within", effect = "twoways", quiet = FALSE)

imp_unemp_spat <- spatialreg::impacts(unemp_spat, tr = trMatc, R = 200)
summary(imp_unemp_spat, zstats=TRUE, short=TRUE)

```

### Labour force spatial
```{r, cache = TRUE, eval = FALSE}

fmdiff_lf <- diff_log_lf ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp + diff_log_pop
lf_spat <- spml(fmdiff_lf, data = allcomp_full, index = NULL,  listw = fmatlw, 
                         lag = TRUE, na.action = na.fail, spatial.error = "b",
                         model = "within", effect = "twoways", quiet = FALSE)

imp_lf_spat <- spatialreg::impacts(lf_spat, tr = trMatc, R = 200)
summary(imp_lf_spat, zstats=TRUE, short=TRUE)
```

### Population spatial
```{r, cache = TRUE, eval = FALSE}
fmdiff_pop <- diff_log_pop ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp
pop_spat <- spml(fmdiff_pop, data = allcomp_full, index = NULL,  listw = fmatlw, 
                         lag = TRUE, na.action = na.fail, spatial.error = "b",
                         model = "within", effect = "twoways", quiet = FALSE)

imp_pop_spat <- spatialreg::impacts(pop_spat, tr = trMatc, R = 200)
summary(imp_pop_spat, zstats=TRUE, short=TRUE)
```

### Table of calculated impacts of overall economy spatial models (SARAR)
```{r, cache = TRUE, eval = FALSE}
tab2 <- cbind(spat_output(impemp_spat)[-4], 
      spat_output(imp_unemp_spat)[-4],
      spat_output(imp_lf_spat)[-4],
      spat_output(imp_pop_spat)[-4])

kable(tab2[,1:6], booktabs=TRUE, format = "latex") %>%
  add_header_above(setNames(c("", 3, 3), c("", "Employed Persons", "Unemployed Persons"))) %>%
  kable_styling(position="center")

kable(tab2[,7:12], booktabs=TRUE, format = "latex") %>%
  add_header_above(setNames(c("", 3, 3), c("", "Labour Force", "Population"))) %>%
  kable_styling(position="center")

```


# (Potential) useful figures/maps {.tabset}
### SSR
Don't see immediate cause for concern? Perhaps some clustering in the southwest/appalachia but the counties themselves are also smaller in size so might optically trick?
```{r}

glht(main, linfct = "mines_diff + lag_diff + lag_diff2 = 0") %>% summary
glht(main_cc, linfct = "mines_diff + lag_diff + lag_diff2 = 0") %>% summary

ssr_df <- allcomp %>% slice(main$obs_selection$obsRemoved) %>% mutate(residuals = main$residuals) %>%
  group_by(fips) %>%
  summarise(ssr = sum(residuals^2))

allcompva <- df_va(allcomp)

ssr_df_va <- df_va(ssr_df)
plot_usmap(data = ssr_df_va, values = "ssr", regions = "counties", col = "black", size = 0.07, exclude = c("AK", "HI" )) +
  labs(title = "Geographical Distribution of SSR") + 
  scale_fill_viridis(name = "ssr",  na.value = "seashell2") +
  theme(panel.background = element_rect(colour = "black", fill = "gray90"))

hist(ssr_df$ssr)

```

### UER
```{r}

means_overall <- allcomp %>% 
   group_by(fips) %>%
   summarize(uer = mean(uer), REE_scaled = sum(REE_inv_scaled_realgdp))

means_overallva <- df_va(means_overall)

hist(allcompva$mines_diff[allcompva$mines_diff != 0])
summary(allcompva$mines_diff[allcompva$mines_diff != 0])

plot_usmap(data = filter(allcompva, year == 2019), values = "uer", regions = "counties", col = "black", size = 0.07, exclude = c("AK", "HI" )) +
   labs(title = "Geographical Distribution of Unemployment Rate in 2019") + 
   scale_fill_viridis(name = "uer", na.value = "seashell2", direction = -1, option = "magma") +
   theme(panel.background = element_rect(color = "black", fill = "gray90"), legend.position = "left")

```

### UER over time
This would ideally be animated or grid arranged but have not yet figured it out :)
```{r}
# Goal: animate or grid.arrange
uer_list = list()
for(i in 2002:2019){
   j = i - 2001
   p1 <- plot_usmap(data = filter(allcompva, year == i), values = "uer", regions = "counties", col = "black", size = 0.07, exclude = c("AK", "HI" )) +
     labs(title = paste0("Geographical Distribution of Unemployment Rate in ", i)) + 
     scale_fill_viridis(name = "uer",  na.value = "seashell2", direction = -1) +
     theme(panel.background = element_rect(color = "black", fill = "gray90"), legend.position = "left")
   print(p1)
   uer_list[[j]] <- p1
}
```

### Other indicators over time
```{r}
yearsums <- allcomp %>%
  group_by(year) %>%
  summarize(REE = sum(REE_inv), TAA = sum(total_taa), active_mines = sum(active_mines), 
            mines_diff = sum(mines_diff), uer = mean(uer), emp = sum(emp), 
            employed = sum(employed), unemployed = sum(unemployed), 
            labour_force = sum(labour_force), labor_hours = mean(labor_hours, na.rm = TRUE))

yearsums$uercalc <- (yearsums$unemployed/yearsums$labour_force)*100

reeplot <- ggplot(yearsums, aes(x=year, y=REE)) +
  geom_line()+
  geom_point()+
  labs(title="Renewable energy investments between 2002-2019",x="Year", y = "Renewable Energy Investments")+
  scale_color_viridis(option = "D") +
  scale_fill_viridis(option = "D")

taaplot <- ggplot(yearsums, aes(x=year, y=TAA)) +
  geom_line()+
  geom_point()+
  labs(title="TAA funding between 2002-2019",x="Year", y = "Renewable Energy Investments")

mines_diffplot <- ggplot(yearsums, aes(x=year, y=mines_diff)) +
  geom_line()+
  geom_line(linetype = "dashed", aes(y = 0))+
  geom_point()+
  labs(title="Change in active mines between 2002-2019",x="Year", y = "Change in active mines")

mines_diff_cty <- ggplot(allcomp, aes(x=year, y = mines_diff)) +
  geom_jitter(aes(color = state.x))+
  geom_point(aes(y = max(mines_diff)))+
  labs(title="Change in active mines between 2002-2019",x="Year", y = "Change in active mines")


emp_lfplot <- ggplot(yearsums, aes(x = year)) +
  geom_line(aes(y = employed, color = "Employed persons")) +
  geom_line(aes(y = labour_force, color = "Labour force"))+
  theme(legend.position = "bottom")+
  labs(title="Employed, Labour Force",x="Year")

unemp_plot <- ggplot(yearsums, aes(x = year, y = labour_force)) +
  geom_line(aes(y = labour_force, color = "Labour force"))+
  theme(legend.position = "bottom")+
  labs(title="Unemployed Persons",x="Year")

uerplot <-ggplot(yearsums, aes(x = year, y = uercalc)) +
  geom_line()+
  theme(legend.position = "bottom")+
  labs(title="National unemployment rate",x="Year")

mines_emp <- ggplot(yearsums, aes(x = year)) +
  geom_line(aes(y = active_mines, color = "Active Mines")) +
  geom_line(aes(y = emp/100, color = "Persons Employed in Coal")) +
  scale_y_continuous(
    name = "Active Mines",
    sec.axis = sec_axis(~.*100, name="Persons Employed in Coal (Hundreds)")
  ) +
  theme(legend.position = "bottom",
    legend.title = element_blank(),
    axis.title.y = element_text(size=13, face = "italic", hjust = 0.5),
    axis.title.y.right = element_text(size=13, face = "italic", hjust = 0.5),
    title = element_text(size = 12)
  ) +
  scale_color_viridis(discrete = TRUE, direction = -1)

grid.arrange(reeplot, taaplot, mines_diffplot, mines_emp, ncol=2, nrow =2)
grid.arrange(uerplot, emp_lfplot, unemp_plot, ncol=2, nrow =2)
```

# Interactive Fixed Effects/Factor Structure Efforts 
## {.tabset}
### Testing for presence of unobserved common factors
Confirmed!
```{r}
library(phtt)

# Create matrices of most often used variables
dep_diff_uer <- c_mat(allcomp$diff_uer)
ind_mines_diff <- c_mat(allcomp$mines_diff)
ind_lag_diff <- c_mat(allcomp$lag_diff)
ind_lag_diff2 <- c_mat(allcomp$lag_diff2)
# ind_closure <- c_mat(allcomp$mine_closure)
# ind_lag_closure <- c_mat(allcomp$lag_closure)
ind_difflogrealgdppc <- c_mat(allcomp$diff_log_realgdp_pc)
ind_difflogrealgdp <- c_mat(allcomp$diff_log_realgdp)
ind_difflogpop <- c_mat(allcomp$diff_log_pop)
dep_logemp <- c_mat(allcomp$diff_log_employed)
dep_logunemp <- c_mat(allcomp$diff_log_unemployed)
dep_loglf <- c_mat(allcomp$diff_log_lf)
dep_logpop <- c_mat(allcomp$diff_log_pop)

################################################################################
# Test for potential interactive FE (beyond additive two-ways fixed effects) as 
# outlined in R package documentation. Result: "Both models have time-varying 
# interactive effects"

# Optimal dimensions = 3 (?) judging by the plots below
fm = dep_diff_uer ~ -1 + ind_mines_diff + ind_lag_diff + ind_lag_diff2 + ind_difflogrealgdppc

test_eup <- Eup(dep_diff_uer ~ -1 + ind_mines_diff + ind_lag_diff + ind_lag_diff2 + ind_difflogrealgdppc , additive.effects = "twoways")
test_kss <- KSS(dep_diff_uer ~ -1 + ind_mines_diff + ind_lag_diff + ind_lag_diff2 + ind_difflogrealgdppc , additive.effects = "twoways")

# Both tests confirm existence of time-varying interactive effects
checkSpecif(test_eup, level = 0.001)
checkSpecif(test_kss, level = 0.001)

################################################################################
OptDim(test_eup, criteria = c("IPC1", "PC1", "PC2"), standardize = TRUE)
plot(OptDim(test_kss, standardize = TRUE), main = NULL, xlab = "Proposed # of Factors")
                                                  
#"Scree Plot: Optimal Factor Choices Reported by Various Factor/Principal Component Analysis Methods"))
# Removing LA counties removes 4-factor suggestion. Only 1 or 2

# kable(test_kss$optimal.dim)

```

### Individual effects only
In preliminary application of phtt package with two-way fixed effects, the third forced factor seemed to follow the behavior of the time-fixed effect. Thus, individual fixed effects were attemped (instead of two-way) with 2,3,4 factors included. However, plotting estimated factors (see plots below) shows that the time-fixed effect 'disappears' (ie. is not accounted for) as the forced factors no longer take up the time-fixed effect behavior of the dependent variable. Thus, I assume that two-way fixed effects is still the way to go with this model. Below, included individual fixed effects models with 2,3,4 factors in case of interest.
```{r phtt efforts without REE Ind}
#| fig.width: 9
#| fig.height: 5

# None of the factors replicate the time fixed effect...thus unlikely that "individual" is the way to go??
# KSS estimation with individual effects only and 2, 3, 4 factors
change1_KSS <- KSS(dep_diff_uer ~ -1 + ind_mines_diff + ind_lag_diff + ind_lag_diff2 + ind_difflogrealgdppc, additive.effects = "individual", factor.dim = 1)
change2_KSS <- KSS(dep_diff_uer ~ -1 + ind_mines_diff + ind_lag_diff + ind_lag_diff2 + ind_difflogrealgdppc, additive.effects = "individual", factor.dim = 2)

summary(change1_KSS)
plot(summary(change1_KSS))
summary(change2_KSS)
plot(summary(change2_KSS))



```


```{r phtt efforts without REE Ind comp}

KSS_ind1 <- dw_compat_kss(change1_KSS)
KSS_ind2 <- dw_compat_kss(change2_KSS)

ife_ind_models <- rbind(KSS_ind1[1:3,], KSS_ind2[1:3,])

```
### Two-way effects
Employing factor structure models using two-way fixed effects yield the following results.

Results:

1. Again, these models corroborate the magnitude and direction of regression coefficients in Models 1 and 1 CC. 
```{r phtt efforts without REE TW}
#| fig.width: 9
#| fig.height: 5
# KSS estimation with individual effects only and 1,2 factors
change1_KSStw <- KSS(dep_diff_uer ~ -1 + ind_mines_diff + ind_lag_diff + ind_lag_diff2 + ind_difflogrealgdppc, additive.effects = "twoways", factor.dim = 1)
change2_KSStw <- KSS(dep_diff_uer ~ -1 + ind_mines_diff + ind_lag_diff + ind_lag_diff2 + ind_difflogrealgdppc, additive.effects = "twoways", factor.dim = 2)

summary(change2_KSStw)
# Results: Regressing UER on change in active mines (+lag) using KSS specification 
# with 2,3,4 factors
#fixInNamespace("summary.KSS", "phtt")
# # Return ee
# n = 55422
# # 
# test1 <- summary(change1_KSStw)
# (1 + log(test1$RSS) +log((2*pi)/n))*(-1*(n/2))
# test2 <- summary(change2_KSStw)
# (1 + log(test2$RSS) +log((2*pi)/n))*(-1*(n/2))

# SSR increase after nfactors = 1.
# To report: 1,2 dimensions

# tabletest <- cbind(tablefun(test1), select(tablefun(test2), c(3)))
# tabletest
# names(tabletest)[3] <- "Factor_1"
# names(tabletest)[4] <- "Factor_2"

# kable(tabletest, booktabs=TRUE, format = "latex") %>% 
#    add_header_above() %>% 
#    kable_styling(position="center")

```


```{r phtt efforts without REE TW comp}
KSS_tw1 <- dw_compat_kss(change1_KSStw)
KSS_tw2 <- dw_compat_kss(change2_KSStw)

summary(change2_KSStw)

ife_tw_models <- rbind(KSS_tw2[1:3,],KSS_tw1[1:3,])

ife_tw_models$term <- substr(ife_tw_models$term, 5, nchar(ife_tw_models$term))

```

### IFE Application of Overall Economy Model
```{r}

emp_KSS <- KSS(dep_logemp ~ -1 + ind_mines_diff + ind_lag_diff + ind_lag_diff2 + ind_difflogrealgdp + ind_difflogpop, additive.effects = "twoways", factor.dim = 1)
unemp_KSS <- KSS(dep_logunemp ~ -1 + ind_mines_diff + ind_lag_diff + ind_lag_diff2 + ind_difflogrealgdp + ind_difflogpop, additive.effects = "twoways", factor.dim = 1)
lf_KSS <- KSS(dep_loglf ~ -1 + ind_mines_diff + ind_lag_diff + ind_lag_diff2 + ind_difflogrealgdp + ind_difflogpop, additive.effects = "twoways", factor.dim = 1)
pop_KSS <- KSS(dep_logpop ~ -1 + ind_mines_diff + ind_lag_diff + ind_lag_diff2 + ind_difflogrealgdp, additive.effects = "twoways", factor.dim = 1)

mergedecon <- cbind(tablefun(summary(emp_KSS))[-2],
               tablefun(summary(unemp_KSS))[3],
                        tablefun(summary(lf_KSS))[3],
                                 rbind(tablefun(summary(pop_KSS)),NA,NA)[3])


kable(mergedecon, booktabs=TRUE, format = "latex") %>%
  add_header_above(c("Variables", "Employed Persons", "Unemployed Persons", "Labour Force", "Population")) %>%
  kable_styling(position="center")

```



### (Ignore atm) PHTT: Replicate regressions incorporating REE interactions
Run on principal regressions with binary mine closure indicator incorporating 
USDA REE investment interaction.

Note: Interaction variables created above via static multiplication of REE 
       binary closure, and lag of binary closure variables. This could 
       potentially pose an issue with SE?

```{r phtt with REE}
#| fig.width: 9
#| fig.height: 5
################################################################################
# Incorporating interaction between REE investments and whether there was a 
# mine closure in that year.

allcomp$intREE = allcomp$REE_bin_top90*allcomp$mines_diff
allcomp$intREElag = allcomp$REE_bin_top90*allcomp$lag_diff
allcomp$intREElag2 = allcomp$REE_bin_top90*allcomp$lag_diff2

test_REE_KSS <- KSS(dep_diff_uer ~  -1 + ind_mines_diff + ind_lag_diff + ind_lag_diff2 + c_mat(allcomp$REE_bin_top90) +
                    c_mat(allcomp$intREE) + c_mat(allcomp$intREElag) + c_mat(allcomp$intREElag2)+ 
                    ind_difflogrealgdppc, 
                    additive.effects = "twoways")

checkSpecif(test_REE_KSS, level = 0.001)
# Rejects null of cross-sectional independence
# Test OptDim: reveals 1,2,4 factors
OptDim(test_REE_KSS, standardize = TRUE)

################################################################################


reebin1_KSS <- KSS(dep_diff_uer ~ -1 + ind_mines_diff + ind_lag_diff + ind_lag_diff2 + c_mat(allcomp$REE_bin_top90) +
                    c_mat(allcomp$intREE) + c_mat(allcomp$intREElag) + c_mat(allcomp$intREElag2)+ 
                    ind_difflogrealgdppc, 
                    additive.effects = "twoways", factor.dim = 1)

reebin2_KSS <- KSS(dep_diff_uer ~ -1 + ind_mines_diff + ind_lag_diff + ind_lag_diff2 + c_mat(allcomp$REE_bin_top90) +
                    c_mat(allcomp$intREE) + c_mat(allcomp$intREElag) + c_mat(allcomp$intREElag2)+ 
                    ind_difflogrealgdppc, 
                    additive.effects = "twoways", factor.dim = 2)

# Results: Regressing UER on mines_diff (+2 time lags) and each of these
# interacted with a binary of whether there was USDA-reported renewable energy investment using 
# KSS specification and 2,3,4 factors
# testree1 <- summary(reebin1_KSS)
# round(testree1$R2, 3)
# testree1$KSS.obj
# (1 + log(testree1$RSS) +log((2*pi)/n))*(-1*(n/2))
# testree2 <- summary(reebin2_KSS)
# (1 + log(testree2$RSS) +log((2*pi)/n))*(-1*(n/2))
# 
# tabletest <- cbind(tablefun(testree1), select(tablefun(testree2), c(3)))
# names(tabletest)[3] <- "Factor_1"
# names(tabletest)[4] <- "Factor_2"
# names(tabletest)
# 
# totaltbl <- rbind(tabletest, data.frame(var = NA,measure = NA, Factor_1 = c(round(testree1$R2, 3), "two-ways", 1),
# Factor_2 = c(round(testree2$R2, 3), "two-ways", 2)))
# testree1

# kable(totaltbl[-2], booktabs=TRUE, format = "latex") %>%
#   add_header_above() %>%
#   kable_styling(position="center")

```

# Coefficient Plots

Results:

1. Main results displayed in coefficient plots show that each model employed for the relationship between the change in active mines and change in unemployment rate estimate the same direction and magnitude of the regression coefficients on the change in active mines regressor (t = t and 1 or 2 time lags). Useful or non-interesting?

## {.tabset}
### FEOLS (General)
```{r}
# Coefficient plots of FEOLS
models <- list(
  "Total US Counties (FEOLS)" = feols(diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc | fips + year, allcomp, se = 'twoway'),
  "Coal Counties Only (FEOLS)" = feols(diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc | fips + year, allcomp_cc, se = 'twoway'))


modelplot(models, coef_map = cm, background = list(geom_vline(xintercept = 0, color = "grey")))+
   labs(x = 'Coefficients (Δ Unemployment Rate)', 
         title = 'Coefficient estimates of FEOLS models')+
    scale_color_manual(values =  viridis(n = 4))+
  theme(panel.background = element_rect("white"))

```

### Spatial models
```{r}
# Coefficient plots of spatial models

full <- tidy(feols(diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc | fips + year, allcomp, se = 'twoway'))
full$model <- "TWFE OLS (Total US)"
cc <- tidy(feols(diff_uer ~ mines_diff + lag_diff + lag_diff2 + diff_log_realgdp_pc | fips + year, allcomp, se = 'twoway'))
cc$model <- "TWFE OLS (Coal Counties Only)"
full <- subset(full, term != "diff_log_realgdp_pc")
cc <- subset(cc, term != "diff_log_realgdp_pc")
full_cc <- rbind(subset(cc, term != "diff_log_realgdp_pc"), subset(full, term != "diff_log_realgdp_pc"))

allmodels <- rbind(spat_models, full_cc[c("estimate", "std.error","model","term")], ife_tw_models)
allmodelssave <- read_excel(here("data/coefplotdf.xlsx"))
dwplot(allmodels, vline = geom_vline(
           xintercept = 0,
           colour = "grey50"
       )) %>% relabel_predictors(
        c(
            mines_diff = "Δ Active Mines",
            lag_diff = "Δ Active Mines (t-1)",
            lag_diff2 = "Δ Active Mines (t-2)"
        )) +
  labs(x = 'Estimates and 95% Confidence Intervals'
       )+
  scale_color_manual(values =  viridis(n = 8), name = "Model Type")+
  theme(panel.background = element_rect(fill = "white"),
  panel.grid.major = element_line(size = 0.1, linetype = 'solid',
                                colour = "gray"))
  
```

### IFE Factor Models
```{r}
# Coefficient plots of IFE models with individual fixed effects only
# Likely incorrect as the additional factors do not mirror the TFE
dwplot(ife_ind_models, vline = geom_vline(
           xintercept = 0,
           colour = "grey60"
       )) %>% relabel_predictors(
        c(
            ind_mines_diff = "Δ Active Mines",
            ind_lag_diff = "Δ Active Mines (t-1)",
            ind_lag_diff2 = "Δ Active Mines (t-2)"
        )) +
  labs(x = 'Coefficients (Δ Unemployment Rate) and 95% Confidence Intervals', 
         title = "Coefficient estimates of IFE models")

# Coefficient plots of IFE models with two-way fixed effects
dwplot(ife_tw_models, vline = geom_vline(
           xintercept = 0,
           colour = "grey60"
       )) %>% relabel_predictors(
        c(
            ind_mines_diff = "Δ Active Mines",
            ind_lag_diff = "Δ Active Mines (t-1)",
            ind_lag_diff2 = "Δ Active Mines (t-2)"
        )) +
  labs(x = 'Coefficients (Δ Unemployment Rate) and 95% Confidence Intervals', 
         title = "Coefficient estimates of IFE models")


```
### FEOLS (By County Type)
```{r}
# Coefficient plot of models on subset of Type 1, 2, and 3 counties
modelplot(model_types, coef_map = cm, background = list(geom_vline(xintercept = 0, color = "grey")))+
   labs(x = 'Coefficients (Δ Unemployment Rate)', 
         title = 'Coefficient estimates of FEOLS models') +
  scale_color_manual(values =  viridis(n = 3))
```


```{r, eval = FALSE, include = FALSE}
# Create spatial residual map

# Create visual of spatial residual map over time

# Spatial impulse response function

```

# Summary stats
```{r}
# Total REE (overall and in CC)
# $6,314,520,585 in overall US since 2002
# $122,238,781 in CC counties since 2002 (1.94%)

natlsums <- allcomp %>%
  summarize(REE = sum(REE_inv), TAA = sum(total_taa), mines_diff = sum(mines_diff))

# Total TAA (overall and in CC)
# $655,349,084,590 in overall US since 2009
# $65,952,811,622 in CC counties since 2009 (10.1%)
cc_sum <- allcomp_cc %>%
  summarize(REE = sum(REE_inv), TAA = sum(total_taa))

# Avg mines closed per yer

```
